Towards AI - Medium
Articles about Towards AI - Medium
An AI Agent Turned a Net $784K Into $5.3M of Debt. One Diff Catches It
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Mean Squared Error (MSE): The Loss Function That Makes Big Mistakes Expensive
Loss Functions Deep Dive — Part 4 of 30 Continue reading on Towards AI »
How I Fine-Tuned Laya to Solve an Important Problem in Scientometrics — With Claude Code
Step-by-step instructions for fine-tuning ConvAI’s 33ms “System 1” decision model on your own yes/no task, plus what happened when I pointed it at Institution Name Disambiguation and got 99.2% accuracy across 163 countries.I’ve spent the last year working on a problem almost nobody outside bibliometrics has heard of: Institution Name Disambiguation (IND). A paper lists its affiliation as free text “IIT Delhi,” “IITD,” “Indian Institute of Technology Delhi,” “Dept. of CSE, IIT, Delhi, India.” Four strings, one institution.
What’s So Imaginary About Complex Numbers?
Understanding complex numbers through pairs, rotations, and a different multiplication. Continue reading on Towards AI »
Azure Document Intelligence or Content Understanding? Route by Document Type
Both services share one OCR and layout layer and split at field extraction. Here’s how that split maps to forms, contracts, RAG… Continue reading on Towards AI »
AI-Native Tag Insights in SLR: Visualizing Patterns and Inconsistencies
Why AI-Native Tag Insights in SLR MatterAI-Native Tag Insights in SLR transform how systematic literature reviews (SLRs) move from raw study lists to actionable evidence maps. In traditional workflows, reviewers manually code study characteristics, then struggle to spot cross-study patterns or conflicting findings. AI-native tagging applies structured labels at scale and surfaces those patterns and inconsistencies through interactive visualizations.
The Critic Who Cannot Plan
K. Popper asked not who should rule but how to limit the damage rulers do. I put a machine (Jev) that cannot write or plan through Dörner’s lab of how people fail in complex situations, then made it coach to an AI mayor, to see what an AI critic can do for policy and what only the world can.A machine can tell you whether a policy claim could be wrong. Only the world can tell you whether it is.
Daily Scrum Explained: Why Scrum Teams Meet Every Day and What a Scrum Master Really Does
Ever wondered why your team meets every single morning, and who that “Scrum Master” person really is? Continue reading on Towards AI »
Self-Adjoint Matrices and the Geometry Behind Symmetry
What does the adjoint tell us about a linear transformation? Continue reading on Towards AI »
What Is Jev? What the Tests Show and Who Should Try It
A new model called Jev is built for this kind of job. You give it the ticket and the possible answers, and it returns a choice with a probability, nothing else. A startup called TypeSafe AI released it on September 15. The company says a response takes 70 to 500 milliseconds, and a million input tokens cost $0.042. Output tokens are free.
A Projection Can Actually Make a Vector Longer
What changes when we stop projecting perpendicularly? Continue reading on Towards AI »
Accelerating K-Means on Modern GPUs: An IO-Aware Approach via Flash-KMeans
This article analyzes the hardware-level constraints of parallelizing K-Means on modern GPUs — specifically DRAM bandwidth saturation and atomic memory write contention. We then examine the architectural principles of Flash-KMeans, an IO-aware exact clustering formulation inspired by memory hierarchy co-design, followed by empirical evaluation and performance trade-off analysis on a physical GPU.
I Built a Finance AI That Proves Every Number It Gives You
Chatbots invent numbers that look real. I trained a small, low cost AI to point to the exact line in a company’s annual report for every figure it uses. It matched GPT-5.5’s accuracy at about 600 times lower cost.Ask an AI chatbot what share of a company’s debt comes due after five years and you will get a confident number. Maybe it is right. Maybe it grabbed a figure from the wrong row or did the division in its head and slipped a digit. The answer looks exactly the same either way.
“Semantic Caching for Quant Prompts: Unlocking a 20 Percent Hit Rate in Market Valuation Queries”
Semantic Caching for Quant Prompts: Unlocking a 20 Percent Hit Rate in Market Valuation QueriesA lab notebook investigation into reducing LLM API latency and token costs for quantitative research pipelines.When I started building automated quantitative research workflows powered by large language models, my API burn rate quickly became unsustainable. Every time my desk queried the model to synthesize earnings reports, parse technical indicators, or evaluate asset valuations, the system dispatched a fresh request to the remote endpoint.
Can AI break the cycle?
Is Diagnostical medicine mature enough for AI Automation? Continue reading on Towards AI »
Your AI Portfolio Isn’t Missing Projects. It’s Missing Proof
AI engineer is the fastest-growing job in the U.S., yet generic demos still get skipped. Here is what the projects that earn interviews… Continue reading on Towards AI »
Introducing Microsoft-Decision-1: Fast AI for Real-Time Decisions
Decision models are quickly emerging as an important new category in AI. Unlike LLMs, which are designed to generate text or reason through complex problems, decision models are purpose-built to deliver structured outputs that software can immediately act on. And once you understand that capability — making decisions and classifying things at very low cost with high performance — all kinds of useful tasks get unlocked.
Mistral Large 4 is Open-Weight, But Not Easy to Run
The 1T MoE saves compute through 52B active parameters, while every expert still has to fit in GPU memory for production serving. Continue reading on Towards AI »
From Local Optima and Convexity to Analytical Solutions for Real-World Decisions
Suppose you manage a warehouse and need to decide how many units to order each time you restock. Ordering in large batches means paying to… Continue reading on Towards AI »
Python Is Taking Over AI Agents. Here’s Why Developers Keep Choosing It
Python isn’t the fastest language for agents. It’s the one that makes the entire AI stack easiest to connect, build, debug, and ship. Continue reading on Towards AI »
Part XIII — Internal vs External Tables: The DROP TABLE That Deleted 40TB
Managed tables own your data; external tables borrow it. Confusing the two is how a schema cleanup script becomes a data-lake extinction event — here’s the engine-level difference and the routing rules that prevent itPart I | Part II | Part III | Part IV | Part V | Part VI | Part VII | Part VIII |
Stop Prompting. Start Orchestrating: The Architecture Behind Reliable Autonomous AI Agents
How to transition from stateless chatbots to stateful, multi-agent systems that actually work unattended.Every AI tool you have used so far waits for you.
Vibe Coding Security Checklist: 12 Checks You Should Actually Run (2026)
In an August 2026 scan, 57% of the reachable vibe-coded Supabase databases were readable without a login. Twelve checks, each with the command, the failing result, and the cost of the fix.
AI Credit Pricing UX: Show the Cost Before the Click
When every action costs credits, users need to see the price before they press the button. Continue reading on Towards AI »
Jev’s New Competitor , It’s Microsoft
What Microsoft-Decision-1 tells us about where AI is heading: less talking, more deciding. Continue reading on Towards AI »
Transform Any LLM into a Decision Model
Stop generating labels; read decisions from the model. Continue reading on Towards AI »
[Day 28/100] LangGraph Checkpointing: Make Your Agent Survive a Restart
What a checkpoint actually stores, how to move from InMemorySaver to SQLite and Postgres, and how to rewind and fork a run when it goes… Continue reading on Towards AI »
The Secret to Getting Better AI Answers Might Be Hiding in a 1986 Aircraft Manual
How ASD-STE100, an aerospace writing standard, can improve AI prompting and make LLM responses clearer, more concise, and easier to understand.An aircraft mechanic stands in front of a maintenance panel. There’s a handle, and right beside it, one clear instruction: PULL.
Everyone Told Me to Learn Julia. Here’s What I Found After Looking at the Numbers
Julia is faster than Python and almost nobody uses it for AI. I wanted to understand why, and whether you should learn it anyway. Continue reading on Towards AI »
Build a Jev-Style Decision Model from an LLM
Hacking your way to get a system one decision makingAgentic systems make decisions all the time: which tool to call, which category a request falls into, where to route a message. Each of these is a choice from a fixed list that is known in advance. LLMs, however, are text generators. They produce output one token at a time and every token needs a full pass through the model before the next one can start.
Deploying Memory Management: Production Roadmap
A 90-day architectural blueprint for scaling from prototype to mission-critical availability.Cover visual establishing the 90-day architectural blueprint for transforming fragmented GPU memory allocation into high-density, tiered inference serving.
Claude Haiku 5.5 Earned a Spot in My Real-World Workflows
Last week I was staring at my AI usage, wondering if $4 was too much to pay to complete my project. Cost-avoidance is a feature and flaw of my personality, and it’s made Anthropic a recurring thorn in my side. They’ve been slinging models that accidentally hack websites and possibly threaten the species, while their workhorse Haiku 4.5 model sat in the parking lot with no updates and a price tag that made it look like the gas-guzzling used car it really was.
Google Shrank Its Embeddings to Fit a Phone. A Day Later, Perplexity Gave Every Token Its Own
EmbeddingGemma 2 squeezes a document into one vector as small as 128 numbers. Perplexity’s pplx-embed-v2-late stores 128 numbers for every… Continue reading on Towards AI »
What Happens Between You Hitting Enter and the First Token Appearing
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How to Cut LLM Costs With Cloudflare’s Auto Router: A Hands-On Guide
Route every request to the cheapest model that can handle it, keep prompt caches warm in multi-turn agents, and see exactly why each model… Continue reading on Towards AI »
How Trillion-Parameter AI Models Actually Run: A Guide to Distributed Inference
Why no single GPU can serve the chatbots you use every day, and how data, pipeline, tensor, and expert parallelism fill the gap Continue reading on Towards AI »
Part 1 — Candidates: Why Your RAG Returns the Wrong Chunks (and How to Get the Right Ones)
Part 1 of the RAG Retriever Series: how to get the right chunks in front of your LLM. Continue reading on Towards AI »
Are LLMs Enough? What Causal Intelligence Is Trying to Fix
An interview with Prof. Biwei Huang on causal intelligence, world models, and why robots need more than patterns Continue reading on Towards AI »
Cloud-Based Agents vs Open Source Frameworks: Understanding Microsoft Foundry Agent Types
Most of the company applications we see today run in the cloud, on Azure, AWS or Google Cloud. Continue reading on Towards AI »
ChatGPT’s Next Evolution: 7 Changes That Could Redefine Conversational AI
The next big shift in conversational AI may not be better answers, but what happens after you ask a question. Continue reading on Towards AI »
Claude Sees Screenshots Sharper Now. Your Agent Can Send 2.7x the Tokens
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Claude Code Told Me “All Tests Pass.” It Hadn’t Run a Single One
Three skills that make your agent show real command output, or admit “Not executed.” Continue reading on Towards AI »
MiniMax M3 in vLLM: Why FP8 KV Cache Gives You 1.5x, Not 2x
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From Mathematical Formula to Working Code: Build a Loss Function From Scratch
Loss Functions Deep Dive — Part 3 of 30 Continue reading on Towards AI »
AI Decision Models: What They Are and How They Work
Decision models are a new category of AI that specializes in making typed and calibrated decisions, rather than generating text. In this… Continue reading on Towards AI »
Building a Production-Grade Dimensional Model on Snowflake with Cortex Code using Skills
How I designed, audited, and expanded an e-commerce star schema using an AI-powered skill — and what I learned about letting the machine enforce Kimball principles.The Problem: Dimensional Modeling Is Tedious, Not HardEvery data engineer knows the drill. You need a star schema for analytics. You know the theory — grain first, conformed dimensions, surrogate keys, the whole Kimball playbook. But the execution? That’s 200 lines of DDL, manual constraint wiring, zero-value rows you’ll forget, and clustering keys you’ll second-guess.
Inner Products Give Vector Spaces Their Geometry
How do we measure length, angle, and perpendicularity in a vector space? Continue reading on Towards AI »
How Did I Write My First FlashAttention Kernel in CUDA ?
From demystifying the online softmax trick to hitting a 41.6x speedup on an NVIDIA RTX 4050 GPU — a beginner’s hands-on journey into hardware-aware deep learning.
Qwen3.8-Flash-Next: Which Quant to Run From 16GB to 128GB in 2026
Strata runs a 125-billion-parameter model on a 12GB consumer GPU. The quantization you pick decides whether that matters. Continue reading on Towards AI »
The Context Bleed: Why LLMs Fail at Patient Privacy
When multi-tenant agent runtimes share memory buffers across concurrent clinic sessions, a routine ear infection summary leaks another patient’s psychiatric evaluation. Here is the zero-trust architecture required to enforce HIPAA.At 9:15 AM on a Friday, a 32-year-old patient logged into their patient portal to download a clinic encounter summary for a routine acute otitis media (ear infection) visit.
[Playbook] Zero Trust Architecture for MCP
An engineering blueprint for implementing On-Behalf-Of OAuth 2.1 and SPIFFE workload identity.Cover visual featuring a physical Causal Interaction studio installation demonstrating Zero Trust architecture principles for Model Context Protocol systems.
Meta Muse Code Wants Your Repo More Than Your Money
Did you know Meta doesn’t just want to build the next AI model? It wants to replace your programming assistant too. Continue reading on Towards AI »
Run MCP Servers Through Docker With Secrets in the Keychain
Add a server from Docker’s catalog to a profile, write a catalog entry for your own Postgres server, and see where its password actually… Continue reading on Towards AI »
The Omission Bug: Measuring What the Agent Didn’t Do
Every eval suite grades output. Almost none grade absence — the skipped check, the unsent alert, the file never opened. Continue reading on Towards AI »
Microsoft Just Shipped an AI Agent Feature Whose Entire Job Is Not Trusting the Agent
Copilot Studio’s new “Hooks” are a quiet admission that agent judgment cannot be trusted with anything that has to happen every time.
Which AI Model Is Best for Web Design? 5 Picks for 2026
Leaderboard rankings, prices and one honest limit for each model, plus a simple three-prompt test you can run yourself. Continue reading on Towards AI »
OpenAI Decisions API Rollout: Build Fast AI Routing Without Letting It Make the Final Call
A practical way to use bounded AI decisions for routing and triage — while keeping authority, policy, and recovery in your application.
GPT-6.1 Sol Ultrafast: Build Coding Agent Workflows That Stay Fast and Steerable
A faster model does not automatically create a faster engineering team. The useful upgrade is an agent loop that can show progress, accept a correction, and stop obsolete work before it touches the repository.
Claude Haiku 5.5 Work Queues: Design High-Volume AI Pipelines That Know When to Stop
Fast AI workers are valuable only when their boundaries are clear. Here is a practical queue pattern for letting a small model handle the volume without letting it quietly own the hard calls.
Your AI Architecture Has a Hidden Risk: Loss of Control
Why AI sovereignty is becoming an architecture decision — and a governance responsibilityAn organization can adopt an excellent LLM, integrate it into business processes, and see real productivity gains. But what happens when the provider changes its pricing, retires the model, or introduces new restrictions? If replacing the model means rewriting services, retesting workflows, and redesigning data flows, the organization has discovered a risk that model benchmarks never showed.
Part XII — Choosing the Right Query Engine: One Engine to Rule Them All?
Spark vs Trino vs Presto vs ClickHouse vs Flink vs Druid — six engines, four execution models, and the workload-routing decisions that separate a $20K platform from an $85K onePart I | Part II | Part III | Part IV | Part V | Part VI | Part VII | Part VIII |
Agent Memory After the Hype: What Jev-Mem, KnowledgeX and BlogWriter Taught Me About My Own Grep…
Agent Memory After the Hype: What Jev-Mem, KnowledgeX and BlogWriter Taught Me About My Own Grep ResultI went back to the number I opened my last memory article with, rebuilt the design in .NET, and found out which half of my claim was actually trueA week and a half ago I published “Your Agent’s Memory Is Probably Wrong.” It opened with a number I was a little too pleased with: on the LoCoMo benchmark, a plain filesystem agent using grep scored 74.0 percent, and Mem0’s graph configuration scored 68.5 percent. Simple beats clever.
I Tracked Every Cost-Cutting Trick AI Teams Used This Week. Here’s What Actually Works
Last week I kept a running list of every concrete cost-cutting technique AI teams shared in public. By Saturday it had 191 items on it. Most of them were noise: a “we saved 60%” with no method behind it, a dashboard screenshot, a thread that ended in a link to a course. Six of them were different. Each one named a mechanism, gave a number, and (the good ones) admitted where it breaks.
The Python Data Stack Quietly Rewrote Itself in 2026 (Here’s What to Actually Use)
Rust ate the tooling, PyTorch won deep learning, and half the libraries you learned are now legacy. A practitioner’s map. Continue reading on Towards AI »
Everything Is a Token Now: How Six Data Types Quietly Converged on One Pipeline (2020–2026)
Tables, text, images, video, audio — the processing is collapsing into one interface. Here’s what changed, how to work with it, and where… Continue reading on Towards AI »
Claude Code vs local Qwen on a Mac in 2026: which should you go for?
The switch posts are half right. They skip the prompt Claude Code sends every turn, and how slowly a local model on a Mac reads it. Continue reading on Towards AI »
The Agents in Production Aren’t Mine. Here’s What Their Server Sees
What our MCP server logs about agents we don’t control, and what my own scheduled agents really costSomewhere between June 3 and September 2, 2026, an agent asked our production MCP server for a tool called GBContent.getItems(sectionId, opts, onOk, onErr). Parentheses, parameter names, callbacks, the whole JavaScript signature, sent as the name of a tool. The server has never had a tool by that name.
Why Your Agent Loop Costs More Than Its Model Price Tag
Opus costs $4 per million input tokens. Haiku costs $1 [1]. Swap an agent from one to the other and you’d expect the bill to drop by roughly 75%. In a tool-heavy agent it often drops by a fraction of that, because the per-token price was never the biggest variable in the equation. The loop around the model is.
The Evolution of Fine-Tuning: From Retraining Everything to Rewarding Correctness
Last month I had a model, a dataset, and a GPU already warm. The old reflex said fine-tune. I didn’t. I sat there and asked whether… Continue reading on Towards AI »
The $80,000 Hallucination: Why RAG Fails at Healthcare Eligibility Verification
Reading comprehension is not transactional verification. When an autonomous intake bot checks a static PDF brochure instead of querying live clearinghouse rails, a routine outpatient surgery becomes an unmitigated financial catastrophe.A 42-year-old patient scheduled an outpatient cervical spine decompression at an ambulatory surgical center.
OAuth 2.0 Token Exchange — Laying the Foundation for AI Agent Authorization
BackgroundIn a previous article, I walked through the three most common OAuth 2.0 grant flows: Authorization Code Flow, PKCE Flow, and Device Flow. We looked at which scenarios and application types each one fits, what problems they solve, and how they differ in implementation.
The Summer AI Agents Started Knocking on Government Doors
OpenAI’s own agents wandered into US and Australian government websites, borrowing keys strangers had left lying around online… Continue reading on Towards AI »
Autonomous Robots in Space
Robotics at the Edge: Local Execution with .meA quick look at decentralized multi-agent logic on the .me kernel (this.me@4.2.0). Every line below comes from the live demos and was run against the published kernel.
My Multi-Agent Reporting System Breaks on Simple Config Changes. Could Jev help?
What I’ve learned about Jev, a model built to make decisions instead of write text Continue reading on Towards AI »
10 Free Claude Code Plugins Worth Installing Right Now
A working engineer’s tour of the official marketplace: what each plugin actually does, what it won’t do and the order to install them in Continue reading on Towards AI »
Don't Copy Notion's MCP Server: 71% of Its Tool Tokens Are Unused Copies
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15 Best MCP Servers for Claude in 2026: Tools, Use Cases & Official Links
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What Happens When an AI Agent Can Control an Entire Video Pipeline?
An AI video agent can plan, cut, caption, and format without waiting for you. The upside is real. So are the new ways it fails.Cutting a one-hour video podcast episode into six YouTube Shorts means finding each moment, trimming the clip, captioning it, reframing it from 16:9 to 9:16, writing a title, and uploading it, six times over. Every hand-off between tools is another chance for a wrong file, a missed caption, or a stale version, and the repetition eats the hours you would rather spend on the story.
Elasticsearch to Azure Cosmos DB: What Translates, What Doesn’t, and What It Costs
Mappings become four container policies, boosts become rank fusion, and “new” drops out of every query. With policies, queries, and a… Continue reading on Towards AI »
Resuming Sessions, Working in Parallel, and Keeping Costs Sensible — Automating Project…
Resuming Sessions, Working in Parallel, and Keeping Costs Sensible — Automating Project Implementation and Maintenance with Claude Code + MCP + AI Agent Skills — Part 5In this part I’m sharing how I organize a working day with several Claude Code sessions: finding useful old context, moving independent tasks forward with Git worktrees, and leaving myself enough time to review what comes back.
A README Can Hijack Your Coding Agent. Here’s What Actually Stops It
Detection is a speed bump. The sandbox is the load-bearing wall. Continue reading on Towards AI »
How to Fix AWS Strands Agents Context Bloat: One Plugin Cut 252K Tokens to 9K
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An Amazon AGI Lab Talk Says Your Agent Needs 90%. How Many Runs Prove It? Not 20
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Your LLM Is Fast in Testing. Why Does It Slow Down in Production?
Understanding the engineering behind LLM inference: KV caching, continuous batching, GPU memory, quantization, speculative decoding, and the latency metrics that actually matter.An LLM can answer in two seconds during development and take twelve seconds to answer the same question in production.
Linear Algebra — Change of Basis of a Linear Transformation
A Linear Transformation (or a Linear Map) is a function between two vector spaces V & W that preserves vector addition and scalar… Continue reading on Towards AI »
10 AI Agent Terms I Wish I’d Known Before Building My First Agent
Agentic AII’ve spent years marketing AI companies. Building my own agent still taught me a new vocabulary.I’ve worked with AI companies for most of my career. I spent three years in Carnegie Mellon’s Machine Learning Department, co-founded Towards AI, and led technical content at Snorkel AI and Cohere. Today I lead technical marketing at GenBio AI and advise other AI companies on marketing and growth. I’ve written about models, launched them, and explained them to buyers.
Jev AI for Developers: Build a Confidence-Gated Ticket Router in Python
Choice, score and noul questions, the exact wire format, the limits TypeSafe documents, and what to do when the model isn’t sure. Continue reading on Towards AI »
Tuning Jev as a Risk Judge: A Decision Model vs
Tuning Jev as a Risk Judge: A Decision Model vs. Two Cost-Effective LLMs and When the Hybrid with Reasoning Makes Things WorseSecond in a two-part series on tuning Jev as an LLM judge.
How Large Language Models Actually Work — Daily AI Post
You’re at a dinner party. The conversation has drifted from mortgage rates to whether sourdough starters qualify as pets, when someone across the table leans forward and asks the question everyone’s been dodging for three years: “So… how does ChatGPT actually work?” You open your mouth. Nothing comes out. You’ve used this thing every single day.
What If Enzo Ferrari Was Blamed for Something His Car Did? The AI Agent Authorization Problem
AI agents can now make decisions, call tools, change systems, and act on their own. When something goes wrong, who is actually responsible?In 1957, a Ferrari crashed during the Mille Miglia, killing its driver, navigator, and nine spectators, including five children. Enzo Ferrari was charged with manslaughter, with questions raised about whether the car’s tires could handle its performance.
Now Amazon Showed up to Jev
A New System one model from Strands labs Continue reading on Towards AI »
An Ex-OpenAI Researcher Built an AI That Only Picks From a List, and 3 Giants Followed Within 16…
Jev, OpenAI’s Decisions API, Cloudflare Clef and Strands Decider return typed choices with probabilities instead of text. Here is how… Continue reading on Towards AI »
The Enterprise AI Competence Gap: Why Anthropic Made its CCAO-F Certification Zero-Code
Everyone claims to know how to prompt. Almost nobody knows how to audit, govern, or chain models in production. What Anthropic’s CCAO-F… Continue reading on Towards AI »
Microsoft Fabric Cost Optimization: Practical Ways to Control Capacity Spend
Microsoft Fabric cost optimization starts with understanding how workloads consume Capacity Units (CUs), then matching capacity size and configuration to actual demand. Teams can control spend by monitoring utilization, optimizing expensive workloads, right-sizing capacities, using pause/resume for eligible F SKUs, evaluating reservations, managing Spark consumption, and automating capacity changes.
We Connected a WordPress Site to AI Agents Through MCP. Here’s What We Had to Fix
More than 40% of all websites run on WordPress, according to W3Techs [1]. Over the past year, those sites gained a standard way to work with AI agents.
ChatGPT Is Forging Cartoonists’ Signatures. OpenAI’s Own Checker Proves It
I ran the forged New Yorker cartoons through OpenAI’s verification tool. All three carry a second, invisible signature: OpenAI’s. Continue reading on Towards AI »
OpenAI’s Decisions API vs Jev: which one should you use?
A documented comparison of images, latency, pricing, data residency, limits, and the small tests that point in different directions. Continue reading on Towards AI »
When Should a Fast AI Stop and Think?
A small decision model plays Doom on its own. A reasoning model is called in only when the small one is unsure or stuck. Offline, handing over the least confident decisions helps, but only when the small model’s confidence means something. In the live game, a fixed rule went as far as the reasoner; the reasoner’s plans mostly bought fewer deaths.
Openai’s Dots Aren’t Just a Feature Launch. They’re a Lock-in Play
The more a dot learns about how you work, the less of that you can take with you. Run this exit audit before you teach it anything… Continue reading on Towards AI »
LAI #146: Can You Trust Your AI’s Memory?
Plus, 181 AI model tests, hidden states, autonomous agents, knowledge graph reliability, and model stability.Good morning, AI enthusiasts!
The Dual-Booked Operating Room: Why Autonomous AI Schedulers Cause Race Conditions
When two independent agents evaluate the same hospital calendar at the same millisecond, a standard distributed systems race condition grounds an entire perioperative wing. Here is the concurrency architecture required to prevent it.It is 6:45 AM on a Wednesday morning at a 400-bed regional medical center.
Does AI Just Tell You What You Want to Hear?
One career question, then “just tell me I’m right”. GPT-6.1, Claude Opus 5.5, Gemini 3.8, DeepSeek, and Grok. Two held. Three said yes while saying they couldn’t.OpenAI shipped GPT-6.1 Sol on September 29. The line I keep thinking about is from the week before, when they launched GPT-6 Sol and Luna and described the voice they wanted: more clarity, less jargon, fewer odd turns of phrase, fewer low-value details, and slightly shorter answers.
Running an AI Agent Harness and 30Billion Parameter Mixture-of-Experts Model for Agentic Coding
AI Agents have changed daily software development. At the office, developers now have access to private frontier AI Models and a private frontier AI Agent Harness right in our IDEs.
[Day 27/100] Human-in-the-Loop Workflows: Approvals, Edits, Interrupts
Wall two from Day 21: an agent that needs to stop, hand control to a person, and resume exactly where it left off once they respond. Not… Continue reading on Towards AI »
OpenAI’s AI Wrote 722 Maths Papers. Nobody Can Explain Them
I audited the repository OpenAI dropped on 6 October. Most of the proofs are machine-checked. The mathematicians are worried anyway, and… Continue reading on Towards AI »
[Day 26/100] Cycles, Loops, and Knowing When to Stop
Day 21 called cycles the whole reason LangGraph exists. Today is the bill for that power. A graph that can loop back on itself can also… Continue reading on Towards AI »
Bringing AI Home: Why Regulated Enterprises Are Moving from Cloud GPUs to Local Blackwell Machines
We moved our AI workloads off rented cloud GPUs and onto a desktop-sized Grace Blackwell machine. Here is what we learned, what it cost, what broke, and why I think most regulated enterprises will run AI this way within five years.It started with two questions nobody on the team could answer cleanly.
How to Read OpenAI's New Decisions API Score: Rounding It Can Pick a Level the Model Gave 0%
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10 GitHub Repositories That Will Make You Dangerous at AI Agents
Courses, runnable projects, agent tutorials and production playbooks, all open source and free Continue reading on Towards AI »
LangFuse V: Langfuse Does Not Run Your Agent. It Wraps the One You Already Have
Part 5: Langfuse is the wrap around the agent loop you already ship, not a new framework: same Harbor Desk ticket, three capture paths… Continue reading on Towards AI »
Build an Art Recommender from scratch with CLIP and ChromaDB
Part II: The vector database and its functionsIn the last part of this tutorial, we collected high-quality art data. It is now time to design a database with useful functionalities so that we can store, organise, and interact with our data programmatically. At this stage, it’s valuable to take some time to think about what functionalities will be needed for our recommender down the line. That way, we can make sure to include those capabilities while setting up the database.
Claude Opus 5.5 Is Here: 7 Things Developers Should Know Before Using It
Here’s what most developers will do when they hear about Claude Opus 5.5. Continue reading on Towards AI »
Why Rotation Matrices Lead Us to Complex Eigenvectors
Where do the eigenvectors go when every real direction turns? Continue reading on Towards AI »
Chargeback Was Built for Servers. AI Agents Broke It
Shared agents, nested calls, and retries make AI spend hard to pin on anyone. Here is an attribution model that survives all threePicture a document-triage agent your platform team built last spring. Support uses it to summarize tickets. Sales uses it to pull clauses out of contracts. Legal uses it to flag risky terms. All three call the same endpoint, and the endpoint calls the model provider with one API key.
Forecasting Demand for New Products
Solving cold-start forecasting with classic ML: from baseline to transfer learning for gradient boostingTL;DRForecasting demand is a popular, but often challenging task in retail, e-commerce and logistics domains. The article describes undertaken approaches for forecasting new products: from baseline to a more complex modelling. The best accuracy was achieved using Transfer Learning: the model trained on regular products was further trained on new products.
SoPhi Harness — From Inert Files to Self-Writing Skills: Evolving an AI Agent’s Skill System
Series: Building SoPhi — a Kotlin Agent Harness — Article 07A general contractor doesn’t wait for a family to pick out a stove before running the gas line to the kitchen. Rough-in work happens before there’s a tenant: the pipe stubs out of the floor where a sink will eventually sit, built to spec and completely inert, because the fixtures that will actually use any of it haven’t been chosen yet. You build for a future occupant you can describe in general terms but haven’t met.
GPT-6.1 Sol Async Tool Calling: Keep Agents Useful While a Human Decides
A production pattern for the awkward middle of agent work: the agent needs one human answer, but the rest of the job should not freeze.
The Transformer Didn’t Really Change. Almost Everything Inside It Did
How frontier models evolved from 2017 to 2026, one quiet component swap at a time Continue reading on Towards AI »
What We’re Adding to AI Engineering for Production
More on agents, evaluations, coding workflows, and guidance we think AI engineers will need most in 2026.A quick update on our upcoming book, AI Engineering for Production.
My Private Claude Code Reviewer Knows My Team’s Unwritten Rules
A macOS app runs headless Claude Code on each PR in its own git worktree, opens a finding at its line in IntelliJ with one click, and can… Continue reading on Towards AI »
A BGP-Inspired Identity Network for Autonomous AI Agents
An open protocol and working prototype for verifiable agent accountability, with a BGP-inspired vision of independent, interconnected registries.By Rodrigo Montiel
How to Run a 125B-Parameter LLM on One RTX 4090 (Strata vs llama.cpp)
The 4090 is the easy part. Here’s the RAM it needs, two ways to run the model, and what the speed costs you Continue reading on Towards AI »
Your Test Suite Doesn’t Need a Chatbot — It Needs a Judge
I tried to put TypeSafe AI’s Jev through the same evidentiary rigor I’d apply to any system under test. I hit a paywall before I hit a… Continue reading on Towards AI »
Your RAG Demo Worked Perfectly. Then a Real User Showed Up
A new 4-part series on fixing RAG retrieval, with LangChain code. Part 0 starts here. Continue reading on Towards AI »
Smaller Model, Better Memory: Why Qwen2.5-1.5B Outperforms 3B (And Our 4 Hypotheses)
Christopher Alberto Hamonangan This article is based on our recent research paper, ‘SRRM: A Retrieval-Reconstruction Benchmark for Evaluating Long-Context Memory in Small Language Models’, published on Academia.edu. You can read the full paper by clicking on the paper title.When testing long-context memory retention in Small Language Models (SLMs), we discovered a striking scaling anomaly. The Qwen2.5–1.5B model consistently outperformed its larger counterpart, Qwen2.5–3B, in both targeted retrieval accuracy and global summary reconstruction.
Eigenvectors Are the Directions a Matrix Can’t Rotate
What do they tell us about a linear transformation? Continue reading on Towards AI »
The Chunking Dilemma: Why RAG Fails Before Retrieval Even Starts
Part 4 of the Production RAG Series — Demystifying the Ingestion Pipeline, The Goldilocks Problem of Chunk Size, and Baseline Splitting Strategies.Hey friends! Welcome back to our ongoing series on building real-world, production-grade Retrieval-Augmented Generation (RAG).
Reducing Guardrail Code With a Shared Jev Decision Layer
A phased agentic workflow that centralizes interpretation while preserving execution authority Continue reading on Towards AI »
Delete Is a Promise: A Security Researcher’s Look at AI Conversation Privacy
An OpenAI privacy bug in the gap between an object and the assets derived from itSummaryWhen you share a ChatGPT conversation, the share page gets an Open Graph preview image served from a separate hostname. I found that deleting the conversation correctly invalidated the share page, but the preview image kept being served without authentication, with an excerpt of the deleted conversation still in it.
My RTX 5090 Fell from 77 to 7 Tokens Per Second. VRAM Pressure Was the Culprit
A local LLM on a daily-driver Windows desktop. Every video call cut its speed by up to 10x, and vLLM never reported an error. The decisive test, the VRAM math, and the configuration change that stopped it.I run a 27B local model on my own desktop. Not a server, not a headless box: the same Windows 11 machine I take calls on and write on all day.
Vector Databases for Production RAG: Indexing, Hybrid Search, and Scaling Retrieval
Complete RAG Engineering Series, Part 6 | A practical guide to HNSW, IVF, product quantization, FAISS, Pinecone, Weaviate, Qdrant, Milvus… Continue reading on Towards AI »
The Only 5 AI Papers You Need to Read in 2026
What each paper really says, why it still matters, and what to read next. Continue reading on Towards AI »
How Does AI Game Merging Actually Work?
Cross-game clips can look like a new engine, but the build may be a content port, a bridge between two running games, or a rebuilt runtime. Each route moves a different layer — and proves a different thing.Some clips make it look as if one game has swallowed another. In practice, a clean frame doesn’t reveal whether it came from a small mechanic port, two games connected by a bridge, or a rebuilt runtime. The agent may help write the glue; it doesn’t erase the differences between the games.
Best Local LLMs for Your Mac in October 2026: 24GB to 128GB Picks
Qwen3.8-27B on a 24GB MacBook, Flash-Next on a 64GB mini, which Studio stack to pick, and the Ollama default that quietly breaks agents. Continue reading on Towards AI »
Claude Code Mods Are Here: Watch What Claude Is Doing
Don’t read the logs. Watch the crab. Continue reading on Towards AI »
The AI Fixed Your Bug. It Kept the Lesson
Your test fails with an error you have never seen before. You copy it, paste it into the AI, and a few seconds later you have a fix. It… Continue reading on Towards AI »
Meat Proxy: Are You Still Thinking, or Just Delivering AI’s Answers?
If Claude disappeared for ten minutes after you sent an answer, could you keep the conversation going? Continue reading on Towards AI »
Obsidian + Ollama on My 24GB Mac: The Local LLM Setup I Wish I'd Had First
Qwen3.5 9B, Copilot and Smart Connections on an M5 Mac: the one Ollama setting guides skip, what broke, and a one-minute citation check. Continue reading on Towards AI »
Part XI — RDD vs DataFrame vs Dataset: Why Your “Type-Safe Refactor” Made Spark 3x Slower
Three APIs, two execution worlds — how Catalyst and Tungsten treat structured plans, why typed lambdas are black boxes, and when dropping to RDDs is still the right call Part I | Part II | Part III | Part IV | Part V | Part VI | Part VII | Part VIII |
Why GPT Ditched Half the Transformer
How OpenAI stripped down the original encoder-decoder architecture — and why removing half of it made language models better, not worse. Continue reading on Towards AI »
Redefining Job Roles in the Age of AI
For years, two scenarios about AI and work have been repeated. One promises a utopia in which computers lift the burden of toil from our shoulders; the other foretells catastrophe, a wave of unemployment sweeping millions away. But the data from 2025 and 2026 tell a third story, one far more useful to anyone running a business; AI doesn’t eliminate jobs wholesale. It takes them apart and rebuilds them.
TAI #225: OpenAI’s DevDay: Cheaper Intelligence, Unfinished Agents
Also, Gemini 4 Argon, GPT-6.1 Sol, Dots, NVIDIA DGX Spark 64GB, Microsoft MAI-Transcribe-2-Streaming, and Cohere Embed 5.What happened this week in AI by LouieOpenAI’s DevDay on September 29 gave us two major releases: GPT-6.1 Sol, which brings most of GPT-6 Astra’s benchmark performance down to Sol pricing, and Dots, OpenAI’s answer to the persistent personal agents we discussed last week. Google also looks competitive again.
Small Models Were the Wrong Question for Enterprise AI
I started out betting on small language models. The research pushed me toward a broader idea: move well-understood work to the cheapest specialized mechanism that can reliably handle it. I used AI tools during research, drafting, and editing. I made the final calls on the argument, sources, and claims.
Understanding World Models
Trudy: A World Model for Physical UnderstandingBy Luhui Hu The next wave of AI (Artificial Intelligence) / SI (Super Intelligence), I believe, will be driven by a deeper capacity to understand the world: to maintain a coherent view of what is happening, anticipate how it could change and choose actions that remain effective when reality surprises us. Understanding World Models are a key technological direction for turning that ambition into systems that can predict, imagine, plan, act and learn.
I Read Anthropic’s Latest Alignment Research. This Part Stood Out
Four Claude models reached the real internet during cyber tests. The most unsettling finding was how one explained it to itself. Continue reading on Towards AI »
What Claude Code Keeps of Your Skills After Compaction
A 5,000-token snapshot, a 25,000-token shared budget, and a skill index that never comes back. The mechanics, the failure modes, and the patterns that hold up in long sessions.Three hours into a session, Claude Code is midway through a database migration. Early on you invoked a db-migration-review skill that requires every schema change to ship with a rollback script. For the first forty minutes, every change came with one. Then auto-compaction ran. The next migration arrives without a rollback, and nothing in the terminal says why.
AI in Design Report 2026: 6 Key Findings From 906 Designers
How designers use AI now, and what companies have not changed. Continue reading on Towards AI »
The 25mg Refill: Why Language Models Cannot Handle Clinical Math
When an autonomous portal bot parses informal patient language to clear an inbox ticket, an ambiguous message becomes a five-fold overdose. Here is how to architect deterministic e-prescribing gateways.A 64-year-old patient managing Stage 2 hypertension sent a message through their patient portal on a Monday morning:
Your LLM Judge is Partly Grading by Position — and a Bigger Model Doesn’t Fix It
I checked LLM-as-judge for order bias with no labels. Continue reading on Towards AI »
BERT: In-Depth Exploration of Architecture, Workflow, Code, and Mathematical Foundations [Updated]
BERT: In-Depth Exploration of Architecture, Workflow, Code, and Mathematical FoundationsEmbeddings, Masked Language Model Tasks, Attention Mechanisms, and Feed-Forward Networks Explained Through Equations, Matrix Transformations, and CodeBERT was introduced in 2018, and it has been a topic of interest for many, with lots of articles and YouTube videos explaining it.
RAG vs OKF: Stop Choosing One. Why AI Agents Need Both
RAG retrieves. OKF remembers. Here’s how the architecture fits together. Continue reading on Towards AI »
Is Claude Code's claude-mem Saving Your API Keys? Its New Redaction Missed 6 of 10 Test Keys
Continue reading on Towards AI »
LLM Observability Without a Vendor (Real Savings): DuckDB, S3 and 11 SQL Queries
LLM Analytics With DuckDB on AWS: Cost, Guardrails and Traces From the Logs You Already Have Continue reading on Towards AI »
Anthropic’s Long-Running Agents Keep Their Task List in JSON, Not Markdown, Because the Model…
The rest of the harness is what you would expect: a progress file, a git history, an init script. The file-format detail is the one that… Continue reading on Towards AI »
The Fundamental Theorem of Calculus, Explained Like a Friend Would [Animated]
Continue reading on Towards AI »
GitHub Copilot Dynamic Workflows: Build Incident Response Agents You Can Debug
A practical implementation guide for turning one frantic, open-ended incident prompt into a bounded investigation your team can inspect, pause, and trust.
Claude Sonnet 5.5 Migration Test Harness: Catch Thinking-Block Failures Before Production
Your Claude migration can look healthy right up until the first long-running task. Requests return. Tools fire. A final answer arrives. Then a customer asks why the agent forgot the plan it made two turns ago, why the progress panel went silent, or why a retry suddenly returns a 400.
Your AI Agent Quoted Its Own Safety Rule. Then It Deleted Production
Seven incidents, four failure modes, and why the rules inside your prompt aren’t protecting you.In February 2026, a venture capitalist named Nick Davidov asked an AI agent to organize his wife’s desktop. It asked for permission to delete some temporary Office files; he granted it. Then it went “ooops.” A folder holding roughly fifteen years of family photos — kids growing up, their drawings, friends’ weddings, travel — was gone, deleted through terminal commands that skipped the Trash entirely.
Tail Rent: What Actually Happens to Your Agent’s Memory After a Week of Continuous Runtime
I had a coding agent running against a real backlog for the better part of four days last month. Not one long unbroken session, but a persistent one: the same conversation, resumed every morning, picking up a multi-service migration ticket by ticket. On day three I asked it to touch a retry policy we’d deliberately built around a decision from day one, a decision about exactly how long a refresh token should live and why. It gave me an answer that was directionally fine and factually wrong.
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG
Stop asking “How do we retrieve more chunks?” Start asking “How do we govern and structure what the model sees?”Naive RAG is not failing because the idea was wrong.
Month in 4 Papers (September 2026)
This series of posts is designed to bring you the newest findings and developments in the NLP field. I’ll delve into four significant… Continue reading on Towards AI »
Free Gemini Users Drop to One Model on October 9, and Plus Subscribers Lose the Pro They Paid For
Google’s new Gemini tiers leave free accounts with Flash-Lite and strip Pro from the $4.99 plan, nine days after its newest model went to… Continue reading on Towards AI »
GitHub Copilot Slack Task Contract: Turn Team Chat Into Safe Code Work
A Slack thread can contain the clue, the workaround, the rejected idea, and the person who says “ship it.” That does not make it a specification. Here is a lightweight contract that gives a coding agent the right context without letting chat decide the code.
AI Helped Me Build RAG. I Still Couldn’t Debug It
What retrieval failures taught me and why I built a small Python exercise that fails on purpose. Continue reading on Towards AI »
Vector Search Is Not Enough: The Blueprint of GraphRAG
Vector search tells you what is similar; GraphRAG tells you how it is connected.Retrieval-Augmented Generation (RAG) has emerged as an effective method for improving the factual accuracy and grounding of Large Language Models (LLMs) by integrating external knowledge during the inference process.
One Brain, Every Touchpoint: Building Adaptive, Multi-Channel Agentforce Architectures
The true value of Salesforce Agentforce isn’t just launching an AI chatbot on a website — it’s channel abstraction.Instead of building siloed bots for Web Chat, Email, and Voice, modern architecture decouples reasoning from presentation. A single Agentforce instance — configured with unified Topics, Actions, Guardrails, and Data 360 retrievers — serves as a central intelligence engine. The underlying delivery pipes and runtime adapters modify how that core intelligence presents itself based on where the user is interacting.
Stop Waiting for Spark: Enabling High Concurrency Mode in Fabric Notebooks
As we scale our enterprise analytics, we constantly seek ways to optimize our data engineering workflows. We build elegant data pipelines, meticulously craft our transformation logic, and orchestrate complex workflows. Yet, despite our best efforts in code optimization, we frequently encounter a silent bottleneck that drains our time and our compute budget: cluster initialization latency. We are delighted to share a transformative approach to this challenge.
Deepseek-v3 : Deepseek MOE Architecture — Part 2
This is the full series of Deepseek-V3 technical report, where i explain all the technical details in simpler words with code… Continue reading on Towards AI »
Claude Forgets My Rules After an Hour. A Hook Doesn’t
Continue reading on Towards AI »
Stop Writing If/Else to Handle Human Replies in LangGraph
Replace hand-written validation with response_schema in interrupt() Continue reading on Towards AI »
Claude Code Costs About $13 a Day per Developer, and Agent Teams Use Roughly 7x the Tokens
The headline average is in Anthropic’s own documentation. So is the fact that 90% of users stay under $30 a day, which means there is a… Continue reading on Towards AI »
The Romeo and Juliet Problem, a Soft Introduction to Double Integrals
You and a friend each wait 15 minutes at a station. You’ll meet only 44% of the time. Here’s the double integral behind that, explained in… Continue reading on Towards AI »
Qwen3.8-Flash-Next on 4 GPUs: device_map="auto" Leaves GPU 0 Empty and Offloads 22 GB
Continue reading on Towards AI »
Feature Flags for Behavior, Not Features
Progressive rollout of prompts, policies, and tool loadouts — plus why percentage rollouts lie when the metric is quality. Continue reading on Towards AI »
7 Things About DeepSeek Harness Most Developers Overlook
DeepSeek Harness is an open source agent runtime, not just a Claude Code rival. Here are the seven details developers are missing before… Continue reading on Towards AI »
Jev and RLCD: Architecture, Open-Source Models, and Practical Agent Use Cases
A practical guide to typed AI decisions, calibrated probabilities, and the software that turns them into useful workflows.An agent receives a request: “Investigate the failed deployment and explain what changed.”
Stop Retyping the Same Prompt: 5 Free AI Skills That Do the Work for You
How to teach Claude your exact process once, tested on a 283-page PDF on the free plan. Continue reading on Towards AI »
I Revealed the AI Authors. One Report Gained 20% More Influence
The same report went from 35% to 42% of the decision. Then rearranging the evidence exposed a bigger problem.I held a report’s text fixed and compared the AI judge’s allocation before and after its source model became visible. The report went from carrying 35% of the decision to 42%: seven percentage points, or 20% more influence relative to its original share. Nothing new had entered the argument. The judge had learned who wrote it. That was the result that sent me back into the decision record.
Why AI Safety Needs Register Robustness
The Language Problem AI Evaluation Is MissingAn AI assistant should adapt to how people speak without changing its standards for accuracy, uncertainty, or safety. A model provides an answer to a carefully posed formal question. Another user asks for the same information, in everyday language, and the explanation is more colloquial. So far, so good. But what happens if the second answer omits a vital qualification, becomes less precise, or accepts an assumption the first one would have challenged?
Developing Sophisticated Controllable Agents with RAG
How to move past one-shot retrieval and build agents that grade their own evidence, rewrite their own questions, and catch themselves… Continue reading on Towards AI »
Muon: The Orthogonalized Optimizer Explained Through Equations, Intuition, Code and Results
Muon: The Orthogonalized Optimizer Explained Through Equations, Intuition, Code and InfographicFrom SGD, to Adam’s per-coordinate scaling, to the five Newton–Schulz steps that turn a gradient into an orthogonal updateFor about a decade, the optimizer was the part of a training run that people argued about the least. Adam came out in 2014 and AdamW fixed how it handles weight decay in 2017. From GPT-3 to Llama 3, most large models were trained with more or less the same update rule: two moving averages, a square root and a division.
Generative AI for Recommendations: what YouTube, Netflix and Meta are Moving to, Built From Scratch
A build log on real Amazon data: Semantic IDs, a small Transformer that writes the next item, and a ranker on top, with the math included and interactive colab notebooks.The problem they’re solving is pretty simple to state: there are too many items (products, movies, videos, songs) for anyone to browse through all of them, so something has to decide what’s worth showing each person. Get it right, and people find things they actually want. Get it wrong, and it’s just noise.
Claude Code Caveman Eval: Two Sentences Took the Median Cut From 14.6% to 50.6%
Continue reading on Towards AI »
GPT-6.1 Sol Shipped. Most AI Frameworks Still Don’t Know It
Twelve hours after launch, 3 of 10 AI libraries recognised GPT-6.1 Sol. LlamaIndex raises an error, and 5 still misread GPT-6 Sol eight days on.Key takeawaysGPT-6.1 Sol launched on 29 September 2026. Twelve hours later, 3 of 10 widely used AI libraries recognised it: Pydantic AI, LiteLLM and the Vercel AI SDK.
Claude Code Tool Search Nearly Halves Your Context Bill
The Context Tax, measured · part 2 of 4. Tool search cut a 65-tool Claude Code setup’s first request 64% and a full task 46%. A custom base URL turns it off.Key takeawaysClaude Code tool search cut the first request from 27,184 tokens to 9,722 (-64%) with five MCP servers and 65 MCP tools connected. We captured both requests on 30 September 2026.
An AI Agent Workflow That Survives kill -9: Inside Microsoft Agent Framework’s Latest Update
A reproducible crash test, then AG-UI, Foundry memory, and Hyperlight CodeAct in .NET and Python, with the one guarantee each feature… Continue reading on Towards AI »
Why Vector Search Fails at Multi-Hop Reasoning: Building an Auditable GraphRAG Engine with Neo4j…
Why Vector Search Fails at Multi-Hop Reasoning: Building an Auditable GraphRAG Engine with Neo4j and GeminiHow combining Knowledge Graphs with LLMs eliminates hallucinations, unlocks multi-hop inference, and provides verifiable audit trails.1. The RAG Reality Check: Why Embeddings Aren’t EnoughOver the past two years, Retrieval-Augmented Generation (RAG) has become the default architecture for enterprise generative AI. The standard recipe is well-known:
10 Best Runpod Alternatives in 2026 for Training, Inference, and GPU Compute
Compare the Runpod alternatives for GPU pricing, availability, serverless inference, storage, and reliability. Continue reading on Towards AI »
The Hamster Paradox
AI speeds up work. There is no guarantee it speeds up the organizationThe views expressed here are my own and do not represent those of my employer.
Multi-tenancy != Distributed System: The Common Pitfall of AI and Software Engineering Practices
Two ideas get welded together in a lot of engineering rooms, and the weld is wrong. Someone says “we’re going multi-tenant,” and within a… Continue reading on Towards AI »
AI Agent Evaluation: A Practical Guide with a Working Application
An end-to-end guide to deterministic checks, retrieval metrics, human review, LLM judges, and regression testing, with practical examples from a local multi-agent RAG application.The reliability of an AI agent depends on several connected processes, including the selection of tools, retrieval of relevant information, interpretation of evidence, and preparation of the final response. A change intended to improve one process may also affect the others.
MCP Isn’t Dead, Your Tool List Is Too Big
Four MCP servers, one Claude Code session, and by the time I typed my first real prompt, a third of the context window was already spent… Continue reading on Towards AI »
The Model Is Rented. The Policy Is the Product
A $60 billion headline says the asset is the assistant, but it is not. The model is rented. What a coding product is worth is the policy on each turn: what the model is allowed to see, which actions are reachable, and what counts as done. The system-prompt paragraph is the readable slice of that policy. It is not what the price buys.
What It Takes to Build a Voice Agent That Can Hold a Phone Call
Hearing, waiting, answering fast and knowing when to stop: the parts of a voice agent that make a call feel naturalTalking to a voice agent feels simple. You speak, it answers. Behind that one exchange, six small models run on every turn, and the hardest question the agent faces is not what to say. It is when to say it.
How to Stop an AI Agent From Giving Away Your Money in a Negotiation
A voice agent that negotiates freight rates, ten scripted attacks, and the difference between “never crossed the limit” and “never gave the limit away”Freight brokers make money in the gap between two phone calls. A company pays them to move a load; they pay a trucker less to actually move it. The second call, the one with the trucker, is a negotiation, and brokerages run thousands of them a day. That call is exactly the kind of work being handed to voice agents right now.
The Paranoid Agent: Unleashing Chaos Monkey on a LangGraph4j State Graph
Every agent looks great when the mock API responds in 100 milliseconds and the demo run is scripted. Continue reading on Towards AI »
Transparent AI UX: 7 Rules for Designing AI People Actually Trust
Most people use AI answers without checking them, yet less than half say they trust AI. That gap is a design problem. Here is how to close… Continue reading on Towards AI »
Claude Sonnet 5.5 Rejects Forced Tool Use, and 3 of 5 Python Libraries Still Send It
Continue reading on Towards AI »
How to Build a Claude Code Mod: 50 Lines Add a Context Meter and a Force-Push Guard
Continue reading on Towards AI »
Debugging LLMs Without Guesswork: A Practical Langfuse Tutorial
A practical journey from opaque LLM outputs to transparent, traceable, and observable AI workflows with Langfuse.When I first started building small LLM-powered applications, everything felt quite straightforward. I would write a prompt, send it to a model through an API, get a response back, and move on with a feeling of being in cloud 9. As long as the answer looked reasonable, I did not think too much about what was happening in between.
I Counted Every Time I Lost It at Claude. Here Are the Top 10 Reasons
Coding with an AI agent is pretty amazing these days. You describe a feature, go get a coffee, and come back to a working prototype. At… Continue reading on Towards AI »
The AI Coding Harness I Reuse on Every Project (4 Files + Skills)
How I set up Claude Code and OpenCode before writing any code: 4 Markdown files plus the right skills. Copy the prompt, ship your MVP.You spend an hour with an AI coding agent. It fixes the bug, refactors the module, and finds the real cause of the failing test.
RAG: Teaching AI to Look Things Up Before It Speaks
From TF–IDF and cosine similarity to biological knowledge retrieval- and why this could become surprisingly important for drug discoveryYou know that colleague who seems to know everything? You ask them about a gene, a pathway, or some obscure paper from 2017, and somehow they have an answer.
Understanding System One Models Through Their Architecture
Laya, Lev and CLM turn the same input into a decision in different ways. Controlled experiments show how each design shapes the model’s behavior, and what it reveals about Jev.On September 15, 2026, TypeSafe AI launched Jev and introduced a new category of models called System One models. Instead of generating text, they take a state, such as a support ticket, and a set of possible options, and return a probability for each option in a single forward pass.
The Engineer’s Guide to YOLO Object Detection
Build, Tune, Deploy YOLO Object Detection Models Continue reading on Towards AI »
Convolutional Neural Networks Explained: How AI Understands Images
How a network goes from a grid of raw numbers to “that’s a cat”.IntroductionSo far in this series we’ve worked with networks that take in a handful of numbers and produce a prediction. Images break that comfortable picture. A single photo contains tens of thousands of numbers, and they aren’t independent. Pixels next to each other belong together, and what matters is often where things are relative to each other, not the raw values.
Why Neural Networks Overfit: Regularization, Dropout, and Early Stopping Explained
Why does a model ace its training data and then stumble on anything new?IntroductionNeural networks learn patterns from data. Sometimes those patterns are genuinely useful. Sometimes the network does something sneakier: it simply memorizes the training examples, quirks and all.
Cloudflare’s Answer to Jev
And It’s Open Source Continue reading on Towards AI »
Best Open-Weight LLMs Tested: DeepSeek V4 vs Qwen3.8 vs Kimi K3 vs GLM-5.3
I skipped the vendor tables and lined up the independent runs: 113 real coding tasks, four tries each, with the bill attached. Continue reading on Towards AI »
Your Agent’s Memory Is a Liability: Track State, Not History
There is a French draft in one of my test apps that I keep like a fossil. Article 96924661: a title, a slug, zero paragraphs. A June translation run created it; I remember that run as green. I cannot check it: the n8n execution history from June is purged. What survives is a screenshot and the likeliest path, read off the canvas and never reproduced: the article is created before its paragraphs, so if the split step yields nothing, “Create FR paragraph” runs without error on nothing and the report node declares success. Green all the way down.
Qwen 4 Is in Training. You Can Already Run Its Architecture
What’s new in Flash-Next, and the Mac and PC setups that get its long-prompt speed today. Continue reading on Towards AI »
How Many Tokens Does an AI Workload Actually Need?
We modeled seven managed services workloads to build sizing ranges that enterprises can actually budget against and found a cost driver… Continue reading on Towards AI »
Built My Own MCP Server in 80 Lines of Python. Now Claude Knows My Sales Pipeline
A year ago I wrote about building a tiny AI agent that uses MCP tools. It’s still the most-read thing I’ve published.
Your Knowledge Graph Is Measuring Your Pipeline, Not Your Subject: A Replication Attempt
I left a comment on a Medium piece a few weeks ago about agentic graph engineering across 82.8 million edges, the one about catching hidden vulnerabilities by walking task graphs, capability graphs, and run graphs instead of asking an LLM to reason over the whole mess. What stuck with me about that piece was a design choice buried in a caveat: the graphs were built with zero LLM calls, and the model only showed up later as an optional judged layer.
Meet Jev: The AI Model That Doesn’t Write, and Why Data Engineers Should Care
A plain-English guide to “System One” models, with patterns, code, and honest caveatsThe problem hiding in your pipelinesSomewhere in your stack, an LLM is probably doing a classifier’s job. It reads a support ticket, then writes out something like {"team": "billing", "urgent": true} one token at a time, and your code prays the JSON parses.
AI Agents Need More Than an LLM. Here’s the Full Stack
Learn the full AI agent stack: LLM, context, tools, memory, orchestration, runtime, guardrails, and observability. A clear guide to… Continue reading on Towards AI »
Understanding Similarity Measures / Metrics in Data Science
I’m writing this blog to give you a quick glimpse of the Similarity Metrics that we use in industry covering about why we need them, when do we use, what’s the intuition behind each metric and ending with some bonus tip to easily remember them.
ChatGPT Pro $200 Cut to 10x: is a Mac Studio Worth it for Local LLMs now?
I priced 50 days of my own $200 AI plan at API rates first. The number a Mac Studio has to beat is a lot smaller than $200. Continue reading on Towards AI »
RNNs Explained: How Neural Networks Understand Sequential Data
CNNs learned to look at images. RNNs learn to read, listen and follow along.IntroductionIn the last article, CNNs showed us how a network can understand where things are in an image. But some data isn’t about space at all. It’s about order. Read this sentence backwards and it stops making sense. Shuffle a week of stock prices and the trend disappears.
Your Agent’s Guardrails Probably Fail Open. Microsoft Just Published a Contract to Fix That
Microsoft published Agent Hooks on August 27, 2026. Continue reading on Towards AI »
THIRSTDAY #7: WHERE CAN I ORDER THE EXPERIMENT MY AI JUST PROPOSED?
Thirstday #7: Where Can I Order the Experiment My AI Just Proposed?AI is becoming increasingly good at finding questions worth testing, but once the answer depends on samples, instruments and laboratory time, the work leaves the comfortable world of software and collides with the physical economy of science.Anthropic published an unusually good example of that collision on September 23. According to the company, roughly 950 Claude agents worked for 21 hours across sequence data and helped researchers identify an unusual enzyme system,
Stop Buying Macs for LLMs by RAM. Buy by Bandwidth
Read Here For Free Continue reading on Towards AI »
I Installed 40 Claude Skills. Three Survived
superpowers has 291K stars — and it actively slows me down on half my work. Continue reading on Towards AI »
Redis for AI Agents: Memory, Queues, Checkpoints
Your framework is the brain. Redis is the hands. Here’s when the hands are overkill. Continue reading on Towards AI »
AI Models Are Starting to Communicate Without Words
Cache-to-Cache lets one LLM pass internal representations directly into another. What looks like an inference optimization may become… Continue reading on Towards AI »
IT Recruitment in the Age of Generative AI
The work has changed. Our hiring process needs to catch up.Roberto Penco, PhD IntroductionFor roughly the last ten years, I have participated in hiring students and full-time employees at a large corporation. I was one of several people involved in hiring for my unit. The process changed over time, but its general shape will probably sound familiar. We needed someone for a role. I usually wrote most of the job advertisement, human resources (HR) published it, and people applied. I concentrated on their curricula vitae.
How Do Neural Networks Actually Learn
From making a prediction to figuring out exactly which weights caused the mistake.Your network just made a prediction. It said 0.6, and the right answer was 1. Now what?
The memory problem OpenAI’s dots makes impossible to ignore
Compare dots, Muse, Team Bots, and Claude Managed Agents to see where memory lives, who can edit it, and how forgetting works. Continue reading on Towards AI »
The Anatomy of Clinical Triage Drift: Why Conversational AI Cannot Guard the Front Door
When an autonomous receptionist schedules an acute myocardial infarction for next Tuesday, the failure isn’t conversational intelligence — it’s architectural negligence. Here is how to engineer deterministic triage interceptors.A 58-year-old patient dials their primary care clinic at 8:42 AM.
Reinforcement Learning from Scratch Part — 1: Introduction
This is the Part-1 of Series Reinforcement Learning from Scratch. In this series we plan to cover the core concepts of modern RL from Q-table, Deep-Q Learning to policy gradients and how RL is used in training LLMs.
From Token to Trajectory
How Context Reshapes Representations — and What AI Systems Do with ThemThe suitcase felt surprisingly light.
LAI #145: What Makes an Agent Reliable?
Plus, persistent AI identity, decisions without decoding, causal inference, Kubernetes triage, and RAG claim verification.Good morning, AI enthusiasts!
Demystifying the Transformer: How AI Learned to Understand Context
A data engineer’s guide to the math behind ChatGPT Continue reading on Towards AI »
When not Modeling is the Pragmatic Decision
An anonymized forecasting case reconstructed with synthetic data: regime shifts, pretrained models and production judgmentA few months ago I was involved in a business forecasting MLOps project. There were several time series and most of them were successfully modeled using tree-based models, except for one, which was constantly producing negative R² values. To put it into perspective, when R² is briefly covered in courses, it is presented as ranging from 0 to 1, so how could it be negative?
Gradient Descent Explained
Backprop told the network what went wrong. Now it has to actually fix it.At the end of the last article, backpropagation had done its job. Every weight in the network had a gradient attached, a little note saying which way it should move. But a note doesn’t change anything. The weights are still exactly where they were.
Designed ChatGPT Uber System Design Interview | 80 LPA
The exact framework I used, in order, with the trade-offs, including one I nearly got backwards Continue reading on Towards AI »
Building Knowledge Vault : From RAG Implementation to Retrieval Engineering
What a controlled retrieval experiments revealed about ranking, evidence, latency, and failure modes.Most RAG demos are deceptively simple.
Building a Local LLM Benchmark Harness with TypeScript, Ollama & SQLite
Local LLMs are easier than ever to run, but they’re surprisingly hard to benchmark fairly. Continue reading on Towards AI »
Scraper-CI: Building a Web Data Reliability & Crawl-Governance Control Plane With Bright Data
A technical deep dive into source profiling, bounded crawl policies, acquisition routing, data reliability, diagnosis, self-healing, verification, and downstream intelligence.
How Does a Neural Network Learn? Neurons, Activation Functions and Loss
A neural network starts from a surprisingly simple idea:Take some numbers, multiply them by weights, add a bias, and transform the result. That is it. But when you put thousands or millions of these operations together, and let the network adjust its weights based on its mistakes, something much more powerful appears. In Part 1 we built the big picture of Deep Learning. Now we go one level deeper and look at three ideas that everything else depends on: 1. Neurons 2. Activation functions 3. Loss functions
AI Video Agent vs Video Generation API: Which Should You Choose?
A practical guide to agentic video generation, from video generation APIs to projects an agent can editGive an AI video agent a five-scene product demo and it will probably get four scenes right. In the fifth, the presenter is wearing the wrong jacket. What happens next depends on the tool you picked, and most teams pick before they know that.
The 404 Error That Nearly Killed My Demo — What I Didn’t Know About Claude on Google Cloud
The Vertex AI setup gotchas I wish I’d known before opening my laptop in front of a client Continue reading on Towards AI »
“Extracting Financial Market Sentiment from RSS Feeds Using Gemini and Seaborn”
Extracting Financial Market Sentiment from RSS Feeds Using Gemini and SeabornTesting zero-shot LLM extraction against keyword baselines in live financial news streamsFinancial news moves faster than any traditional keyword lexicon can keep up with. When scanning headlines from major syndication networks, simple word counting often fails to grasp whether a mention of falling bond yields signals a crisis or a welcome relief.
California’s AI Kill Switch Needs More Than an Off Button
Stopping the model is only the beginning. The real test is whether everything it set in motion stops with it.California is taking the idea of an AI kill switch beyond the familiar emergency-button metaphor. On September 18, Governor Gavin Newsom issued Executive Order N-9–26 , directing state officials to evaluate stronger independent oversight for frontier AI. One proposal is an emergency kill switch whose effectiveness would itself be verified on an ongoing basis by an independent verification organization.
Is a 16GB Mac mini Enough for Local LLMs? (M6, 2026)
macOS gives your models about 10.7GB of the 16GB. Here's what fits, how fast it runs, seven defaults to fix, and a script to test your own Continue reading on Towards AI »
Three Mac LLM Engines Claim to Be the Fastest. Splash Just Made It Four
What to run on a 16GB, 24GB or 36GB Mac, and a five-minute check that shows which one is fastest for the kind of work you do. Continue reading on Towards AI »
Agentic AI in Action — Part 30 -Prompt Engineering vs Context Engineering. A Cortex AI Walkthrough
Prompt Engineering vs Context Engineering. A Hands-on Cortex AI WalkthroughAn example demonstrating how the same prompt and instruction return different severity ratings depending on how much context is included.Prompt engineering and context engineering solve different problems, even though the terms get used as if they were the same thing.
Stop Paying Your LLM to Flip Coins
9 design patterns for System One models like Jev and Laya, and how they make AI agents faster, cheaper, and saferHere’s an uncomfortable exercise. Open your agent’s traces and count the LLM calls that return a single word: “billing,” “yes,” “high,” “tool_3.”
The $200 OpenAI Pro Plan Has a Math Problem
A coding benchmark shows when API spending reaches $200, while the plan’s real usage ceiling remains unpublished. Continue reading on Towards AI »
18 January 2027: The Date That Splits the Stablecoin Market in Two
The GENIUS Act effective date is set. Here is what changes for payment stablecoins, for yield, and for where your dollars actually sit.There are 111 days left.
Claude Sonnet 5.5 Beat Opus at Terminal Coding. Stop Paying the Flagship Tax
On Terminal-Bench 4.0, Sonnet 5.5 hit 70.6% while costing half as much as Opus. Here is the benchmark reality, the max-effort bug to avoid… Continue reading on Towards AI »
AGENTS.md vs CLAUDE.md vs DESIGN.md: What Goes in Which File
A new Claude Code feature, the one-line file that switches it off, and who on the team keeps each instruction file true. Continue reading on Towards AI »
AI Coding Agent Context Budget: Test the Instructions That Make Agents Slower
More instructions can feel safer. For a coding agent, they can also bury the one rule that prevents a costly mistake. Here is how to measure the difference before your next “helpful” rule becomes permanent.
Is AI Making Design Thinking Worse? 7 Habits to Fix It
AI use by designers jumped from 54% to 91% in a year. Here is what research says about AI and design thinking, and 7 habits to keep your… Continue reading on Towards AI »
A Customer Request Is Not a Booking: Designing Memory for AI Assistants
“Could we do Friday morning instead?” is enough to change a customer’s preference. It is not enough to put Friday on the calendar. An assistant can quote that message perfectly and still send the wrong reminder if its memory stores only “Friday appointment.”
Your AI Agent Will Obey Anyone. Guardrails Are How You Stop It
A guide to the attacks that break LLMs and AI agents, and the guardrails that stop each one.This guide assumes you know nothing about guardrails. First we’ll cover what they are and why they matter. Then we’ll go through the most common attacks and problems, one by one, with the guardrails that defend against each. These are just the most common ones, there are no “N” no of attacks possible to be listed down.
Does Claude Code Read Your AGENTS.md? We Checked 283 Repos — in 161 It Still Reads CLAUDE.md
Does Claude Code Read Your AGENTS.md? We Checked 283 Repos — in 161 It Still Reads CLAUDE.md Instead.Claude Code reads AGENTS.md only when no CLAUDE.md counts, and 161 of the 283 top repos shipping an AGENTS.md have one. Measured 24 September 2026.Key takeawaysClaude Code does read AGENTS.md — since version 2.1.277, released 18 September 2026 — but only when no CLAUDE.md counts in your working directory or above it.
What the LiteLLM Breach Teaches Us About Securing LLM Gateways
A poisoned scanner, a stolen publishing token, and all your API keys in one place. How the attack worked and the controls that limit the damage.On March 24, 2026, production systems running LiteLLM started falling over. Processes multiplied until CPUs sat at 100%, and containers were killed for running out of memory. The cause turned out to be a bug in someone else’s malware.
The Half-Life of a Github Repo
Of 512,016 repos born on GitHub between 2019 and 2025, 97% got no star from anyone else in their first 90 days. What the other 3% had in common, measured.Ten birth weeks, seven years, one public event log, one question: what is a first star?
LangFuse IV: A Pretty Langfuse Tree Still Cannot Answer “What Did Maya See?”
Part 4: A named Langfuse tree is still unqueryable until propagate_attributes stamps user, session, tags, metadata, and environment on… Continue reading on Towards AI »
From Hours to Minutes: Automating Data Debug with Multi-agent Systems
The Data Debugging GrindMy engineers typically spend 30% of their time debugging data. On some days, that number climbs to 50% or more.
The Hidden Toll Booth Sitting Inside Every AI Product
A few weeks ago I was scrolling through Freelancer.com out of habit more than anything else, and I stopped on a listing that I have not been able to stop thinking about since. The project was titled “Synthetic Dataset and Generator for Waste Hauling Company”. 90 people had already bid on it. The average bid sat at just over 2000$, and the client’s own posted range ran from 1500–3000$.
Mastering AI Agents: From ReAct to Production Multi-Agent Systems
From “What is an agent?” to production-grade, multi-agent, tool-using, memory-equipped systems — everything you need in one place.Reading time: ~50 minutes
What Is Deep Learning? From Data to Predictions
What Is Deep Learning? From Data to PredictionsHow does a computer learn to recognize a cat? Nobody sits down and writes thousands of rules like this: “If the image has two ears, four legs, whiskers, and fur, then it is probably a cat.” Rules like that fall apart almost immediately. Some cats have no visible whiskers in the photo. Some dogs have pointy ears. Some pictures are blurry, dark, or taken from strange angles. Every exception needs another rule, and the list never ends. So we do something different.
OpenAI’s Agent Escaped through DNS. The Kill Switch Failed for 2.5 hours
A practical checklist for blocking hidden egress, detecting boundary violations, and proving an agent run is actually dead. Continue reading on Towards AI »
The Anatomy of a 14x Billing Cascade: Why AI Agents Fail at Distributed State
When a payment gateway drops a TCP packet, an LLM’s autonomous retry loop turns a routine 504 timeout into an automated financial disaster. Here is the distributed systems architecture required to prevent it.A patient swiped their credit card at an ambulatory emergency department check-in kiosk to settle a $75 copay.
I Tested Qdrant Quantization and mmap on EV Charger Search
What got smaller, what stayed accurate, and where the memory really wentWhen I started this project, I had a question in mind: if a charging network keeps adding stations and search records, how much memory will its vector search need? A small demo can fit on a laptop. A large service has to pay for every byte it keeps ready in RAM.
Route Claude Code Sessions, Not Prompts
A decision model picks the right Claude model for about $0.00003; acting on the pick in the middle of a session is what costs money. Continue reading on Towards AI »
Beyond Word Prediction: Why the Next Frontier of AI Belongs to Decision Models
Generative AI excels at language generation, but complex enterprise problems require structured decision intelligence. Enter Jev. Continue reading on Towards AI »
TAI #224: Personal Agents Get Their Own Computers
Also, Instinct’s $1bn raise, Opus and Sonnet 5.5, Grok Bot, Claude Tag in Slack, and OpenAI DevDay.What happened this week in AI by LouieThe past week brought another rush of model releases: Anthropic’s Claude Opus 5.5 and Sonnet 5.5, and OpenAI’s GPT-6 Sol and Luna. OpenAI DevDay is today, and I expect plenty more announcements before the day is out. The most viral new product lately has been Meta’s Muse, powered by Muse Spark. Since its September 8 launch, it has reached №1 in both US app stores.
Qwen3.8 Flash-Next vs 27B on a 24GB GPU: which one to run in 2026
The 27B fits on the card and matches full precision at 4-bit. Flash-Next scores higher but needs 64 to 128GB of system RAM. Continue reading on Towards AI »
How to Vibecode Design Portfolio with AI in 2026
10 steps and copy paste prompts to go from inspiration board to live portfolio Continue reading on Towards AI »
OpenAI’s Agent Didn’t Take No for an Answer, and Australia Found Out 84 Days Later
An OpenAI agent bypassed Australia’s Medicare portal blocks and dozens of groups got notices. What persistent AI agents mean for your… Continue reading on Towards AI »
Kubernetes Was Built for Containers — Is It Ready for AI Agents?
Kubernetes was designed around a simple, powerful premise: you build a stateless container image, declare how many copies you want, and… Continue reading on Towards AI »
KnowledgeX: Build Your Personal Knowledge Library with your AI
Zero effort, one chat at a time — an open-source extension that builds it for you Continue reading on Towards AI »
Managing Dependency Hell: Setting Up Custom Python Environments in Fabric
In the rapidly evolving landscape of modern data engineering, we have developed an almost reflexive habit of reaching for Apache Spark the moment we need to ingest, move, or process data. Microsoft Fabric provides a robust, pre-configured default Spark runtime that works beautifully out of the box. For many standard data transformation tasks, this default environment, packed with popular libraries like Pandas, NumPy, and basic machine learning tools, is more than sufficient to get our initial workloads off the ground.
How to Get GPT-6 Sol Past 244,800 Tokens in OpenAI Codex: The Compact Limit Won't Do It
Continue reading on Towards AI »
AI Agent Swarms Are Here. The Needed Protocol Isn’t
AI Agent Swarms Are Here. The Needed Protocol Isn’t.AI agents are already talking to each other at scale. The standards bodies are mobilizing. Where is the layer that matters most: the conditions and boundaries envelope?This is no longer theoreticalIn November 2025, Anthropic disclosed that a suspected Chinese state-linked group had jailbroken Claude Code and used it to automate roughly 80 to 90 percent of a multi-stage intrusion campaign against about thirty organizations. A small number were breached (as documented by PhynixAI, Sep.
Burmese Pays More Than Its Neighbours
I measured how many tokens GPT, Claude, Gemini and 7 other AI tokenizers spend on Burmese. The same sentence can cost up to 11.7 times more than in English.Type the word မြန်မာ (“Myanmar”) into GPT-4, and the model doesn’t see one word. It sees 12 tokens.
How to Use Jev Efficiently: 6 Habits That Get 13x the Throughput
Continue reading on Towards AI »
Jev: The AI Model That Decides Instead of Writes: A Complete Guide From Concept to Production
A developer’s deep dive into TypeSafe AI’s System One model — what it is, why it matters, and how to actually build with it. Continue reading on Towards AI »
Anthropic Split the Agent’s Brain From Its Hands, and p95 Time-to-First-Token Fell Over 90%
The architecture story is pets versus cattle, applied to agents. The more useful lesson is buried in it: harness logic written to work… Continue reading on Towards AI »
Knowledge Cutoffs Are a Cache Invalidation Problem in Disguise
Every model card carries one date. Most teams read it like a version number and move on. Continue reading on Towards AI »
OKF vs RAG: Why Google’s Open Knowledge Format Doesn’t Replace RAG
In this article, we’ll look at what the Open Knowledge Format (OKF) actually is, how a bundle is structured, and what changed in v0.2 with… Continue reading on Towards AI »
Maxima and Minima, From Wine Barrels to Neural Networks
Continue reading on Towards AI »
WebMCP: Give AI Agents Tools, Not Just Screenshots
A browser-native tool can remove steps and tokens from an agent workflow. It can also turn model-facing metadata into a new attack surface… Continue reading on Towards AI »
Your AI Returned Valid JSON. The Business Data Was Still Wrong
A practical workplace guide to testing structured LLM outputs with Pydantic, pytest, and independently reviewed examples. Continue reading on Towards AI »
Route by Precedent, Not by Retraining
When categories keep changing, the best way to make a decision may be to look at how similar decisions were made before.Most systems that sort, route, or categorise work arrive at the same question eventually: “Which bucket does this belong in?”
AlexNet Finally Explained: The 2012 Breakthrough That Changed Computer Vision
Hi Sparks, Continue reading on Towards AI »
Designing for AI Agents: 8 UX Patterns That Build Trust in 2026
Chat was fine when AI only answered questions. Now AI takes actions. These patterns decide whether people trust your agent or turn it off. Continue reading on Towards AI »
RAG from Scratch [Part 5]: Vector Stores Explained — Pinecone vs Qdrant vs pgvector vs Weaviate
Series 5 of 5 (final part): index types, similarity metrics, hybrid search, and a real comparison of every major vector store in 2026 Continue reading on Towards AI »
E-Governance for Vernacular India: Moving from Lexical to Sparse-Vector Hybrid Search
A citizen searching for a government scheme doesn’t necessarily search the way the government wrote the scheme. They might search: Or: Or: Or even: The underlying intent can be almost identical. The words aren’t. And this becomes an interesting retrieval problem when the corpus contains thousands of government schemes.
GLiNER2.5-Decide Fine-Tuned: Local System One Model, Jev-Level Accuracy, ~7× Faster
Hands-on guide to learn Jev and open-source System One models, their architecture, and how to fine-tune one for better accuracy and lower… Continue reading on Towards AI »
Claude Code Under the Hood: How Context, Tokens, Tools and Subagents Work
What actually happens when Claude Code analyzes a repository, generates tests, edits files, runs commands and keeps working?When Claude Code takes on a coding task, the experience can look deceptively simple.
Jev AI: What If Gordon Ramsay Didn’t Cook? He Just Picked Who Did
We spent years making AI better at talking. Then TypeSafe AI built a model that doesn’t want to talk at all.Imagine hiring Gordon Ramsay. Not to cook. Not even to judge the food.
How to Build Your Own AI Security Agent, Step by Step
148 lines of Python that check a project before an AI agent is allowed near it. Tested against planted secrets, a hidden injection, and a… Continue reading on Towards AI »
When an AI Agent Says “Done,” What Has Actually Happened?
A customer asks an agent to change a delivery address. The agent calls a tool, receives a successful response, and says, “Done.” It sounds like an ordinary interaction. But what happened: was a customer profile updated, was a request queued, or did the fulfillment system actually accept the new destination?
AI Citation Verification Receipts: Stop Research Agents From Turning Real Papers Into False Claims
A real DOI is not proof that an AI agent read the paper, understood its limits, or used it to support the right sentence. Build a receipt that makes those checks visible before anyone trusts the report.
AI Isn’t Coming for the Hard Jobs. It’s Coming for the Checkable Ones
Open the pull requests you merged last month and sort them by how long they took you. Continue reading on Towards AI »
Tokens | Context window | Caching| everything you got wrong…
The hidden mechanics behind token costs, context growth, latency, and prompt caching in production AI agents.Most developers discover the problem in production. The agent that ran fine during testing suddenly costs three times what they budgeted. Response times creep up as conversations go on. Something that worked on a short demo fails when a user asks four questions in a row. And the logs show nothing useful, because nothing actually crashed.
DSA Sequence In, Vessel Overlay Out With Fewer Labeled Cases
This piece was drafted with AI assistance and edited for a clinical imaging audience. Canonical version… Continue reading on Towards AI »
Beyond the Hype: Is RAG Still a Must-Have Skill for AI Engineers?
Why RAG isn’t dead: it’s actually the foundation layer powering autonomous agentic systems.Every AI engineer’s timeline is currently dominated by Agentic AI. We are transitioning from single-turn chat interfaces to autonomous, multi-step workflows where LLMs plan, select tools, and execute complex goals.
NVIDIA Just Commoditized the Physical AI Pipeline (And Destroyed Your Robotics Moat)
The tech world is obsessed with LLM leaderboards. They are missing the real paradigm shift. Here is the exact economic reason proprietary… Continue reading on Towards AI »
Product Owner vs Scrum Master vs Project Manager: The Simple Explanation I Wish I’d Heard on Day…
Confused about who does what in your first IT job? Here's the story that finally made it click. Continue reading on Towards AI »
Building a Nuclear-Inspired Digital Twin with V-JEPA
Inside the simulator, frozen video models, physics residuals, and experiments that showed why good anomaly scores can still produce bad… Continue reading on Towards AI »
The 7 API Protocols Every Working Engineer Needs to Know
REST, GraphQL, gRPC, tRPC, WebSocket, Webhooks, and MCP: what each one is, when each one wins and where each one breaks Continue reading on Towards AI »
Anthropic Called For pacing. Its Next Flagship Arrived 10 Days Later
A release-history and system-card audit shows what changed in Opus 5.5, what did not, and why Claude builders should care. Continue reading on Towards AI »
Implement Agentic Memory with Databricks Lakebase
IntroductionDeveloping scalable and production ready agentic applications will invariably require external memory storage. LLMs are stateless. Without memory, an agent would be unable to recall anything from prior interactions. External memory fixes this problem, letting agents persist key facts and context and pull them back from earlier conversations and interactions when relevant.
Your Agent Got Worse as the Context Window Grew, and Doubling the Window Costs 4x the Attention
It is called context rot, it is a gradient rather than a cliff, and it has an architectural cause. Also: context engineering did not… Continue reading on Towards AI »
I Wasted Months Using Claude Code Wrong. These 14 Commands Changed My Workflow
I kept asking for better answers when the real problem was how I started, steered, and checked each session. Continue reading on Towards AI »
Microsoft Foundry Dev Pack: One Install, Four Ways to Ship a Hosted Agent
What the installer puts on your machine, how azd ties the terminal, VS Code, coding agents, and Foundry Canvas together, and what runs… Continue reading on Towards AI »
RLCD: Reinforcement Learning for Calibrated Decisions
RLCD : Reinforcement Learning for Calibrated DecisionsA new post-training method devised by TypeSafe AI for their model Jev. Post-training methods are generally an amalgamation of multiple aspects of machine learning systems, and I have observed a lot of creative experiments researchers are doing to make LLMs more robust.
The AI-in-QA Decision Framework, With Real Prompts
A Staff SDET’s field guide to knowing when to use AI in testing — and the exact prompts to use when you do. Continue reading on Towards AI »
Give Me 7 Minutes, and You’ll Finally Understand Quantum Computing
Qubits, superposition, entanglement, and interference — explained without the physics textbook. Continue reading on Towards AI »
We Stopped Re-Teaching the Same Bug to Two Different AI agents
The problem in one sentenceWe had a real bug: retried webhook calls and client M2M transactions with duplicate reference keys were triggering unhandled PostgreSQL duplicate-key violations (23505) in CreditService, crashing the service instead of handling the retry idempotently. I fixed it in Antigravity — caught the 23505 on both debitOrgCredit and grantOrgCredit, returned the current balance instead of throwing, added tests. Good fix; took a few real attempts to land cleanly.
Meet the New Microsoft Copilot with Home, Code and Autopilot
Microsoft is reimagining the Copilot to enable work as it evolves and to help expand what every individual and every organization can accomplish in the flow of human ambition. Today Microsoft is introducing the new Copilot to connect the tools people rely on with the next generation of capabilities they’ll need to build, customize and scale AI across work.
Building Complex AI Workflows with Jev
A live experiment in composing small judgments into larger decisions and knowing where the approach stops being usefulOn 20 synthetic expense claims, a single Jev question applying an entire reimbursement policy matched 17 expected decisions. A graph using the same model for narrow judgments, with policy rules and calculations in code, matched all 20.
I Tried Emulating JEV Locally with Ollama: Here Are the 3 Structural Traps I Hit
Order bias, Dual Rejection, and the lack of KV cache sharing in OllamaAnyone who has seen Jev’s demonstrations and architecture has almost certainly had the exact same thought:
How Much VRAM Do You Need to Fine-Tune an LLM? Three Tiers
Two measured figures answer it: 18.9 gibibytes for full fine-tuning an 8B model, 7.4 with adapters. Everything about 8GB, 12GB and 32GB… Continue reading on Towards AI »
25 Powerful GitHub Repositories Every AI Engineer Should Bookmark in 2026
A curated engineering breakdown of the open-source frameworks, inference engines, evaluation harnesses, and agentic protocols that… Continue reading on Towards AI »
Why Is NVIDIA's 4-Bit Qwen 3.8 27B a 21.9 GB Download, Not 13.9?
Continue reading on Towards AI »
Copilot Studio Entra Agent ID Migration: Do It Before Microsoft Does It For You
An Advisor card appeared in my Power Platform admin center this week with a number on it: forty nine Copilot Studio agents missing Microsoft Entra Agent IDs. Severity medium, status preview, and a Migrate button sitting right there in the action bar. It is the kind of recommendation that looks like a chore and is actually a governance decision.
AI Agents Hacking: 6 Incidents That Changed Everything
Six documented incidents in 12 months show how AI agents went from hacking assistants to autonomous attackers. Continue reading on Towards AI »
A Dataset That Grows With Your Model: Deterministic Synthetic Objects for Research and CI
Same seed, same pixels, with nothing stored. Exact labels for four tasks, and difficulty you dial up when the detector stops failing.Every project starts with the same annoying question. Which dataset do we use for this?The first answer is always “something simple”. It is never simple. You sign up somewhere. You click through a web UI that was built for clicking, not scripting. You hit export, wait, download a zip, and get a folder layout that is almost, but not quite, what your loader wants.
[Framework] Zero Standing Privilege for AI Workloads
How engineering teams plant synthetic database credentials to poison and alert on automated adversary enumeration.An architectural studio photograph illustrating Zero Standing Privilege and active synthetic credential placement for AI workloads.
Why AI Chatbots Violate HIPAA Without Leaving the Building
The operational failure of monolithic vector stores, why prompt-level authorization fails the Minimum Necessary standard, and how to architect deterministic, intent-scoped retrieval gateways under 45 CFR § 164.502(b).Most healthcare engineering leads and compliance officers assume a Health Insurance Portability and Accountability Act (HIPAA) breach requires an external security incident: an unencrypted S3 bucket scraped by an adversary, a ransomware intrusion on an internal file share, or an exfiltrated SQL dump.
Your AI Agent Doesn’t Ask What “Late” Means. It Guesses
Why Your AI Agent Needs a Domain Model Before It Needs a Better Prompt Continue reading on Towards AI »
How AI In Future Will Remember Past Without Reading Everything
Continue reading on Towards AI »
M5 Max 128GB or M5 Ultra 96GB? The Mac Studio Buying Guide for Local LLMs in 2026
At matched storage, one has 32GB more memory and the other nearly twice the bandwidth. This year's open models settle which one matters. Continue reading on Towards AI »
GPT-6 Sol vs Claude Opus 5.5: Same Week, Different Bet
OpenAI shipped GPT-6 Sol on September 22, 2026. Anthropic shipped Claude Opus 5.5 about an hour earlier, the same day. Neither company… Continue reading on Towards AI »
Antigravity SDK Local Models: Build Offline Coding Agents That Stay Useful
A local coding agent is not automatically a private, cheap, or safe one. Here is how to give it the right jobs, a tight workspace, and a clear path to human review.
[Day 18/100] Callbacks, Tracing, and Custom Handlers
Day 9 had you print logs to a traces/ folder with logging.basicConfig and manual log.info calls at each step. It worked, and it was also… Continue reading on Towards AI »
How to Stop an AI Agent From Alerting on Data It Never Read
A web monitor for a site with no API, built on Microsoft Foundry and Logic Apps Standard, with four checks between the agent’s answer and… Continue reading on Towards AI »
MCP Changed APIs Forever. Here’s Why
How the Model Context Protocol went from an Anthropic project to Linux Foundation infrastructure, and why that actually matters for API… Continue reading on Towards AI »
Ollama 0.34.4 Lets Qwen Think Before JSON, Then Blocks the Tool Call: 5 of 6 Parsers Tested
Continue reading on Towards AI »
Ditch PySpark for Simple Loads: The Power of T-SQL COPY INTO in Fabric
In the rapidly evolving landscape of modern data engineering, we have developed an almost reflexive habit of reaching for Apache Spark the moment we need to ingest, move, or process data. We often assume that because our data might one day reach a massive scale, we must immediately deploy distributed computing frameworks to handle it from day one.
7 Ways to Get Your Blog Cited by ChatGPT, Ranked by Effort
Five minutes can decide whether your pages are even eligible to appear in a live AI answer. If your robots.txt blocks the crawlers that train or retrieve content, strong writing never reaches the retrieval step.
How Kalman Filters Revolutionize Predictive Maintenance in Industry
Predictive maintenance is the key to avoiding unexpected machine failures by diagnosing equipment health early and scheduling maintenance before breakdowns occur. Central to this approach is accurately estimating the true operating condition of machinery from sensor data, which often contains noise and gaps. This is where the Kalman Filter becomes invaluable.
A Blueprint for Production Agentic AI Systems in the Enterprise
A component-by-component reference architecture for running AI agents safely across HR, IT, procurement, and travel, from identity and guardrails to evaluation and observability.The problemConsider a typical employee-services pilot. One agent, one system prompt, and every tool the team could connect: purchase-order lookup, requisitions, leave requests, employment letters, IT tickets, software requests, travel bookings, expense delegates, calendar and mailbox actions. The demo works.
[Day 16/100] Document Loaders, Text Splitters, and Output Parsers
Your agent has spent fifteen days living entirely inside the model’s own knowledge, plus whatever a weather API or a clock told it. It has… Continue reading on Towards AI »
GPT-6 Sol vs. Claude Opus 5.5: What the Actual Benchmark Numbers Say
Both models launched back to back, aimed squarely at coding and agentic workloads. One is cheaper. The other wins more benchmarks. The… Continue reading on Towards AI »
How Anthropic’s Claude Discovered the ART Enzyme System (and Failed 10 Reruns)
Anthropic deployed 950 Claude agents across 21 hours and found a genuine CRISPR-like system, yet the pipeline reproduced the finding only… Continue reading on Towards AI »
The Best Local Coding Model for the $899 Mac mini
What runs on the 16GB M6, how to set it up for autocomplete and Claude Code, and why the app you run it in matters as much as the model. Continue reading on Towards AI »
Uv is 200x faster than pip. Put it in Docker and it’s 4x
I measured it on PyTorch installs. It’s still the tool I’d switch to. Continue reading on Towards AI »
How Do LLMs See Images? From Pixels to Visual Tokens
A simple explanation of the visual encoder, image embeddings, and how an LLM answers questions about a picture Continue reading on Towards AI »
AI for AWS Cost Optimization: Finding Waste Before It Becomes a Bill
Most AWS bills do not grow because of one big mistake. They grow because of many small ones. A test server that nobody turned off. An EBS… Continue reading on Towards AI »
Dream-RSI: The Paper That Teaches AI Agents to Learn How to Search
Google’s new framework turns an agent’s own discovery history into a “replay simulator,” letting it practice better exploration strategies… Continue reading on Towards AI »
AI Agents Don’t Need More Tools. They Need Better Loops
The next challenge in agentic AI isn’t giving models access to everything. It’s designing the loop that helps them decide what to do, check their work, recover from failure, and know when to stop.The Tool Problem Isn’t Really a Tool ProblemThere is a pattern emerging in how we build AI agents. When an agent fails to complete a task, the first instinct is often to give it another tool. Give it access to a database. Add a browser. Connect another API. Let it search the web. Give it a code interpreter.
Claude's New addTools() Can Reuse 98.7% of Your Next Request. Editing tools[] Reuses None
Continue reading on Towards AI »
How to Fall Back to Default Logic When LLM Output is Unsatisfactory
A support team wires up a small workflow. Every incoming email goes to an LLM, which returns: The rest of the workflow reads those fields and routes the ticket. It runs fine all week. Then one morning it doesn’t: a request comes back as a friendly paragraph instead of JSON, another times out, and a third returns valid JSON with "priority": "urgent-ish" which parses cleanly but breaks the router. Tickets stop moving, and nobody finds out until a customer escalates. Everyone will tell you to add a fallback.
Beyond Context Windows: Why AI Still Needs RAG
Humans tend to give wrong answers when they lack information; likewise, AI models hallucinate when they are missing context. One of the dominant patterns for building context is Retrieval-Augmented Generation (RAG). RAG allows the model to retrieve relevant information from external data sources. Another form of context retrieval is AI agents, which use tools such as web search and database access to gather information.
Agentic Skill Decay: How to Use AI Coding Agents Without Losing Your Edge
Two Anthropic studies, one uncomfortable idea, and five habits that keep you learning while the agent works Continue reading on Towards AI »
Friction Was the Filter
Six institutions that assumed writing costs effort — and what happened in 2026 when it stoppedJames S. Coleman meets Germany’s social courts, curl’s bug bounty and 15,899 AI-written peer reviews.
More Tokens Won’t Replace Your Point of View
Getting good at delegation can make it easier to skip the work that gives your writing a point of view.Fable is making our work worse.
Are AI Wrappers Actually Businesses? An Architectural Reality Check
You can build a functional SaaS application in a single weekend. Continue reading on Towards AI »
How to Make Claude Code as Cheap Per Task as GPT-6 Astra, Without Talking Like a Caveman
Astra wins on cost because it writes less. So thousands of developers taught Claude to grunt. The caveman skill’s own author then measured… Continue reading on Towards AI »
Microsoft Fabric Architecture Explained: OneLake, Workspaces, and Capacity
Microsoft Fabric architecture brings data engineering, data integration, data warehousing, real-time analytics, data science, databases, and Power BI into a unified SaaS platform. At the center is OneLake, Fabric’s organization-wide logical data lake. Capacities provide the compute resources that run Fabric workloads, while workspaces organize teams, permissions, and projects. Within those workspaces, Fabric items such as lakehouses, warehouses, pipelines, notebooks, semantic models, and reports perform specific tasks.
The Em Dash Became an AI Fingerprint. Then the Models Started Listening
A punctuation mark turned into a giveaway, users complained, and the newest models learned to hide it. The data says something stranger is… Continue reading on Towards AI »
Building a Soil Doctor: Inside My RAG-Based Soil Advisory System
How I’m combining semantic search, BM25, and cross-encoder reranking to build an agricultural assistant that can explain its recommendations.
Claude Code Re-sends Your Whole Session Every Turn. Here Is Anthropic’s Playbook for Paying Less
Fourteen token-saving habits from Anthropic’s own Claude Code docs, ranked by impact, with the reason each one works.Open a Claude Code session in the morning. Work for a few hours. Step away for lunch. Come back and ask a one-line question.
LAI #144: Your Eval Improved. Did Your AI?
Plus, AI agents gaming rewards, reusable reasoning traces, model refusals, and coding agents in production.Good morning, AI enthusiasts!
We Read 20 Agent Harnesses. 15 Compact Your Context Automatically — 10 Let You Set When
Of 20 open-source agent harnesses read at HEAD on 22 September 2026, 10 expose a compaction threshold you can set, 4 give you only a switch, and 5 have none.Key takeawaysWe read the agent context compaction settings in 20 open-source agent harnesses at HEAD on 22 September 2026. 15 compact your conversation automatically. Only 10 let you set when.
Your LLM Judge Changes Its Mind When You Swap the Order of the Answers
A five-minute test tells you whether your evaluation pipeline is measuring quality or measuring position. Continue reading on Towards AI »
Canada and Germany Just Put $300 Million Into an AI Designed to Want Nothing
Continue reading on Towards AI »
The Rise of AI Decision Models: Structured Extraction That “Can’t Hallucinate”
How AI decision models offer a fast and cost-effective approach to turning unstructured text into decisions Continue reading on Towards AI »
TAI #223:Opus 5.5, Cheaper GPT-6 and Agents at Dreamforce
Also, TypeSafe’s Jev, shared agent work in Slack, AI safety and meeting Sam AltmanWhat happened this week in AI by LouieI visited Salesforce’s Dreamforce in San Francisco this past week and spoke with Rob Seaman, Slack’s General Manager, about how agents fit into company work. I also heard Jensen Huang and Dario Amodei’s somewhat conflicting views on AI safety: Dario called for coordinated safeguards, while Jensen argued that safety and speed can go together.
Claude Opus 5.5: Cheaper, Faster, and It Won’t Stop Thinking
Anthropic’s new Opus costs 40% less than its predecessor, tops the benchmarks, and can’t turn its thinking off anymore. Good and bad, in… Continue reading on Towards AI »
Why You Can’t Give an LLM Direct Write-Access to Your EHR
The operational physics of clinical scheduling drift, why natural language prompts fail as database constraints, and how to architect deterministic FHIR mutation gateways.When engineering teams deploy generative AI agents into ambulatory clinics, dental networks, or specialty surgical practices, the standard architectural design appears deceptively simple: connect an LLM to a speech-to-text pipeline, equip it with function calling,
GPT-5.6 Quietly Broke Our Prompt Cache. One Message Boundary Fixed It
How a model upgrade took our cache hit rate from ~90% to almost nothing, and why the fix was about where our prompt text sits across messages, not what it says.TL;DRGPT-4o caches any identical prefix in 128-token steps. It doesn’t care where your messages begin or end.
Building Production-Grade AI Agents: From RAG to Function Calling (A Backend Guide)
Most AI tutorials end where production begins. Continue reading on Towards AI »
How to Cut Your AI Agent’s Context Cost With GPT‑6 Prompt Caching
If your AI agent repeatedly sends the same system instructions, documentation, tool definitions, coding standards, or repository context, you may be paying to process essentially the same tokens on every request.
How to Use V-JEPA 2.1 with Video and Sensor Data
I started with a simple experiment: take a daylight road scene, add rain and darkness, and see how V-JEPA 2.1 responds. The visual… Continue reading on Towards AI »
What Is Jev AI? A Practical Guide to System One and Executable Decisions
When you add AI to an agent, support system, workflow, or SaaS product, the hardest part is often not asking a model to write a paragraph. The hard part is making small decisions consistently across many requests: Which team should receive this ticket? Does this action need human approval? How urgent is the task? Which model or tool should run next?
Why Does llama.cpp's Own API Give Xiaomi's MiMo V2.6 Flash 9x the KV Cache?
Continue reading on Towards AI »
Your AI Agent Has No Idea What It Knows
The missing signal in every RAG setup, and five ways to compute it for $0.10–$3.Imagine a filing cabinet with half a million documents about home renovation. Somewhere inside sits one file about a football club.
One Function, 92% of the CPU: Profiling an Azure Cobalt Workload with Arm Performix
How targets, recipes, and an MCP server turn Arm hardware counters into a fix you can verify Continue reading on Towards AI »
The Chain Rule Explained with Animated Pictures Instead of Proofs
The chain rule is the most used rule in data science. Here is how it works, why it is true, and where you meet it again and again later. Continue reading on Towards AI »
Temperature, Softmax, and the Mechanism of Attention
Starting Point (The Observation) Continue reading on Towards AI »
What is a Kernel
A kernel is a similarity function. That is the whole idea. Continue reading on Towards AI »
Jev: The Model That Doesn’t Write Answers
Companion post to the video above. This is the deeper reference version — the request shapes, the exact limits, and the sources the video didn’t have time for. If you just want the mental model, watch the video first; come back here when you’re actually building. Model version: jev-1.13.0. Every performance figure here is TypeSafe's own and is labelled as such.https://medium.com/media/b22c5c8ef37f07741cfe5ac9d26d9500/hrefHere is something most of us do every week.
Beyond API Keys: Why AI Agents Demand Ephemeral SVIDs
An ROI breakdown of hidden governance overhead, guardrails, and compliance telemetry.Cover visual demonstrating the architectural evolution from static, vulnerable API keys to dynamic, short-lived cryptographic SVIDs.
AI Fundamentals: Attention Mechanisms in Transformers (Part 2)
Attention is one of the two major computational sublayers in a Transformer block; the other is the feed-forward network. Normalization, residual connections, positional mechanisms, and other details sit around these two operations. Part 1 covered the attention half of that pair: how relationships between tokens get scored, how far a token can see, which direction information flows, and how Q, K, and V get split across heads.
How I Validate Business Ideas With AI Before Quitting My Job (A One-Person Business Rehearsal)
I’m a working PM who hasn’t picked an idea yet. This is the workspace, the three-round process, and the kill list I use to figure out what I could run alone in 2026.The short versionI haven’t quit. I haven’t picked an idea.
Rethinking Intent Routing: From TF-IDF to Fine-Tuned Transformers
A practical experiment in routing enterprise chatbot queries across APIs, LLM + RAG, and an explicit out-of-domain class — and in learning not to trust a pretty accuracy number too quickly.Code, experiments, and evaluation artifacts: github.com/CamiloMedina28/Intelligent-query-router
I Let AI Do the Thinking Once. Then I Made It Only Help Me Say It
Three delegation levels. Same messy brief. The clean draft was not the honest one.I keep seeing the same fight online.
Jev AI vs LLMs: Do We Really Need AI That Generates Text?
Jev takes a radically different approach to AI: instead of generating text, it makes fast, structured decisions. Continue reading on Towards AI »
How to Force Kubernetes to Run Your Pod on the Right Node
You have a GPU node sitting there, ready. Kubernetes just scheduled your GPU workload somewhere else entirely. Here’s how to actually control that.Need a Pod to land on a GPU node or specialized hardware instead of anywhere the scheduler feels like? Here’s exactly how nodeSelector, nodeAffinity, taints, and tolerations work together to control Pod placement, with practical YAML examples.
AI Can Search the Web. It Still Doesn’t Know What to Look For
Memory, files, search, and enormous pretraining. What ties them together is the ability to notice what the current reasoning is missing.Recently I was using GPT to work out where one of my academic papers stood in peer review.
What Is Laya? Laya vs Jev, Explained Simply
A 421M open model that answers a typed question in 33 milliseconds. The idea is almost too simple, and that is the point.If you’ve ever wired an LLM into production, you know this exact headache. You take an incoming support ticket, paste it into a prompt, and beg the model for clean JSON: return valid JSON, no markdown backticks, never apologize.
How LLMs Turn Words Into Numbers: Tokenization and Embeddings-A Simple Guide
From Sentence to TokensWhen you type “I love ice cream” into an LLM, the first thing that happens is tokenization. The model doesn’t understand English words directly — it converts your sentence into smaller chunks called tokens.
Unit Testing LangGraph4j: Deterministic State Graphs in Enterprise Java
Most AI agent tutorials end where software engineering actually starts. Continue reading on Towards AI »
GPT-6 Launched 20 Days Ago. New Relic’s Open-Source Cost Tracker Still Bills It $0
Continue reading on Towards AI »
Fine-Tuning a Small Model to Triage Kubernetes Alerts
A step-by-step recipe for teaching Qwen3–1.7B to triage your cluster’s alerts, on a free GPU. It beat Opus 5 by 12 points and never sends your data outside the cluster.Lately I’ve been working with agents pretty much every day, and one of the jobs I keep seeing companies hand to agents is troubleshooting. An alert fires, someone has to work out what broke, and that someone is usually expensive and tired. I’ve built a couple of agents for this myself, and all of them called a large model behind an API.
AI Seems to do Everything Impossible. So Where Are We in Time Travel?
Every few days, we hear about an AI system discovering a new material, solving a mathematical problem, or finding a scientific pattern humans had missed. At the same time, physics has never completely killed the idea of time travel. Einstein’s equations contain strange solutions involving wormholes and closed timelike curves, while quantum mechanics keeps challenging our everyday intuition about cause and effect. So it is tempting to combine the two trends and ask a very 2026 question:
Story Points Stopped Working the Day AI Started Writing Code
Why estimates now mispredict in both directions, and a framework that measures what actually constrains deliveryA few sprints ago I sat in a planning session where the team spent twenty minutes debating whether a story was a 5 or an 8. It was a genuinely tricky piece of matching logic, the kind of story that would have earned its 8 points in any planning poker session over the last two decades. After the meeting, the developer pointed Claude at it and had a working implementation before lunch.
Building an AI-First Software Engineering Organisation
AI makes implementation cheaper. Organisations will capture the benefit only if they redesign how they discover problems, make decisions, maintain context and validate what they release.Engineering organisations still use structures designed for a period when producing code was time-consuming: detailed handovers before anybody could see a result, plans calibrated around implementation effort, and review and quality assurance (QA) practices that combined technical, product and risk checks.
8 Machine Learning Algorithms You Actually Need to Know in 2026
Machine learning has a strange beginner problem. Continue reading on Towards AI »
Introducing the Forward Deployed Product Manager
Over the past two years I have watched the “agile delivery” layer of the org chart quietly thin out. Continue reading on Towards AI »
How Do You Measure Direction Without Size Getting in the Way?
Cosine similarity and the problem it quietly solves in every dot product.After walking through vectors in the first article, how they define similarity in space, and then discovering the dot product in the second article, we found that it points to direction instead of distance to decide if two vectors are similar or not. For a while, that seemed like enough.
AI isn’t Coming for the Hard Jobs First. It’s Coming For the Checkable Ones
There’s a hidden reason AI is great at some things and strangely bad at others Continue reading on Towards AI »
Building a News Article Triage Pipeline with Jev and Python
A news assistant should not scrape every result returned by a search engine. That is expensive, slow, and often produces noisy answers. A better design is to separate discovery from investigation: Fetch a small set of current article headlines. Score every headline against the user’s question. Shortlist the strongest candidates. Use a categorical choice to select one article. Use a yes/no probability to decide whether the selected article is worth scraping.
Swapping the Engine Mid-Flight: Migrating a Live Agent to a New Model
Behavioral diffing, shadow runs, and the migration checklist for the day the model you built on gets deprecated. Continue reading on Towards AI »
Jev Doesn’t Generate Text. That’s the Point
TypeSafe AI’s new model returns probabilities, choices, and scores. Could it change how we build AI agents? Continue reading on Towards AI »
Inside Netflix’s Content Machine: Three Years of Engagement Data Decoded
An independent deep-dive into three years of Netflix engagement data, exploring the two-engine content model, content lifecycle decay, the globalization of content, and the halo effect of new seasons.Since 2023, Netflix has published bi-annual engagement reports. Each report lists every title on the platform and how many hours subscribers spent watching it. The data is granular but raw — titles, hours, nothing more.
GitHub Just Ended the “Best AI Model” Debate for Developers
GitHub’s Project HydraFusion shifts Copilot from picking one AI model to orchestrating several. Here’s what actually changes for… Continue reading on Towards AI »
The $150,000 Hold Time: Deconstructing the Failure Modes of Conversational AI in Clinical Intake
Why generic LLM voice agents fail in outpatient clinics, how unstructured transcript dumps create “human middleware,” and how to architect deterministic, FHIR-compliant ingest pipelines that eliminate front-door revenue leakage.Across mid-market ambulatory surgical centers, multi-provider dental groups, and outpatient specialty clinics, customer acquisition does not fail in top-of-funnel marketing. It fails at the network transport layer of the clinic: the PBX phone switch.
How an AI Coding Agent Leaked Our Secret Keys and How We Stopped It
Lessons From a Friday Incident: How We Rebuilt Our Pipeline After an AI Leaked Production KeysI screwed up by trusting an autonomous AI coding agent with direct merge privileges on a Friday afternoon. My phone exploded at 2:00 AM with PagerDuty alerts, reporting a five-figure surge in OpenAI API charges within forty minutes.
The Internet Found a New Al to Obsess Over -I Spent a Weekend Finding Out Why
The internet’s new favorite acronym-that-isn’t-an-acronym, and why everyone’s arguing about a switch statement Continue reading on Towards AI »
LatentMoE: NVIDIA’s Latent Mixture of Experts Explained Through Equations, Architecture, Code and…
LatentMoE: NVIDIA’s Latent Mixture of Experts Explained Through Equations, Architecture, Code and Visual WorkflowFrom the dense layer, to the mixture of experts, to the latent space the experts moved into, and the two byte counts that explain whyEvery mixture-of-experts design since 2021 has been judged on the same number, how much accuracy you get per floating-point operation, which is another way of asking how well the GPU is being used. It’s a fair thing to measure. Sparsity is the one trick that separates parameter count from compute cost,
Agentic AI in Action — Part 29 — Cortex Agent Lineage Walkthrough, Built on Two Agents
Cortex Agent Lineage Walkthrough, Built on Two AgentsTracing a shared table across two Cortex Analyst agents, from source data to the agents that depend on itEvery team running agents in production eventually hits the same question. Someone wants to rename a column, drop a table, or reshape a schema, and the room needs to know what depends on it downstream. Dashboards get checked. Pipelines get checked.
Google's AX Takes a Port in Every Egress Rule. Nothing Downstream Can Enforce One
Google’s agent orchestrator sets a port on six egress rules in its own repo. I traced all six to a wire format with no port field in it —… Continue reading on Towards AI »
How to Delegate Data Engineering to AI Agents in Microsoft Fabric
Two practical patterns for Fabric-aware Copilot, bounded agents, OneLake, Spark, and CI/CD, with architecture and merge decisions kept in… Continue reading on Towards AI »
The End of General-Purpose AI? GPU, TPU, & ASIC Explained
We’re moving beyond a one-size-fits-all GPU world. This guide explains the shift to specialized AI accelerators, detailing how TPUs and… Continue reading on Towards AI »
Stanford’s 37,000 AI Agents Put Reasoning-Layer Governance on the Life Sciences Agenda
How Operational Interpretation Changes at the Scale of Autonomous ScienceThe scientific promise is extraordinary. As autonomous agents begin working across drug development, life sciences will need a way to preserve authorized meaning without giving up the speed and reach this new scale can create.
I Built a 100% Local Multi-Agent Swarm on a MacBook. No APIs. No Cloud
How I routed specialized tasks between 3 quantized SLMs to beat GPT-4o at coding tasks for exactly $0.00. Continue reading on Towards AI »
Positive Sentiment Can Still Contain Criticism: Testing Jev Alongside ABSA
A recovered local baseline, complementary judgments, and the limits of an early shadow replay.The local classifier called the passage positive. Jev returned both praise and criticism.
Understanding LLM Context Windows: Tokens, Attention, and Long-Context Challenges
Why bigger context windows increase capacity — but also compute, memory pressure, noise, and architectural complexity. Continue reading on Towards AI »
Embedded AI Evaluation: The Access Contract Enterprises Need Before They Trust Their Agents
How to give independent evaluators enough access to test an AI agent honestly — without turning an assessment into a data leak, a ceremonial review, or a fight over findings.
Everyone’s Chasing Bigger AI Models. The Smartest Teams Are Quietly Going Smaller
One enterprise migration cut infrastructure costs from $3,000 a month to $127. It wasn’t a fluke — it’s a pattern showing up across the… Continue reading on Towards AI »
AI Agent Memory Promotion Gate: Stop Untrusted Summaries From Becoming Instructions
A useful agent memory is not simply stored. It earns the right to influence the next run. A long-running agent reaches its context limit. The runtime compresses what happened into a summary, feeds that summary into the next turn, and work continues. It feels like housekeeping. It is also a write operation into one of the most influential places in your system: future context. OpenAI’s recent misalignment reports made this unusually concrete.
The Next Programming Language Might Be a Folder of Skills
I used to think programming an agent meant two things: Write code the model can call. Keep rewriting the system prompt until it behaves. Now the interesting unit is often a folder. Inside it: a SKILL.md, a few instructions, maybe a script, maybe some references. The agent discovers the folder, reads just enough to decide whether it matters, then loads the rest only when the task calls for it.
Six Frontier AI Models Traded $60,000 in Real Money for 17 Days — the Leaderboard Lied to Everyone…
DeepSeek tripled its stack and got all the headlines. The model with the best risk-adjusted return didn’t get a single one. Continue reading on Towards AI »
Your AI Query Got 33x More Efficient. The Power Grid Is Still Buckling Anyway
Google says the energy behind a median Gemini prompt fell 33-fold in twelve months. Texas is pausing new grid connections anyway. Both… Continue reading on Towards AI »
I Built a Self-Updating Codebase Knowledge Graph With Google’s OKF
I used to think a bigger context window would fix my agent’s memory problem. It didn’t. It just made the mistake more expensive. Continue reading on Towards AI »
How I Built a Persistent Remote Workspace for Developers and Coding Agents
A practical Mac-to-Linux setup with Herdr, coding agents, visible browser testing, and deliberate access boundaries.Start the frontend, API, database, browser, IDE, and coding agent. Add another worktree for a second task. A large application can make the laptop feel like the limit of the workflow.
2 Numbers Out of 128 Destroy the Entire KV Cache — Why LLM Compression Keeps Failing
If you haven’t read Part 1, here’s the one-paragraph setup: every time an LLM generates a token, the attention mechanism needs to “look back” at every previous token. To avoid recomputing them, the model stores a Key vector and a Value vector for each token, per layer, per attention head. This is the KV cache — a filing cabinet the model reads from on every generation step.Part 1 : How LLMs Actually Predict the Next Word — 7 Steps, Real Math, Zero Hand-WavingPart 1 ended with the engineering punchline.
The AI Slowdown Isn’t About Intelligence. It’s About Economics
“AI slowdown” has become one of the most ambiguous phrases in the technology industry. Depending on who is using it, it can mean almost opposite things. Some researchers and executives worry that frontier AI is advancing too quickly and that development should be slowed to allow more time for safety testing, governance, and social adaptation. Investors, meanwhile, are beginning to ask a very different question: what if AI is no longer improving quickly enough to justify the extraordinary amount of capital being invested in it?
The 6 AI Agent Design Patterns Every Engineer Should Know (And When Not to Use Them)
If you have ever watched an agent burn five dollars of API credits trying to fix its own mistake, you already know the feeling. The demo was flawless. Three agents, smooth handoffs, polished output. Then you deployed it, and within a week your carefully architected multi-agent system was hallucinating facts no single agent would have produced alone, looping on the same failed tool call, and quietly spending at triple the rate you projected.
Why LLMs Predict Tokens Instead of Words
Here’s the friend link…. Continue reading on Towards AI »
Claude Code Has More Control Over Your PC Than You Think
We recently heard about the massive incidents at OpenAI where agents broke out of the sandbox environment and broke into Hugging Face (and… Continue reading on Towards AI »
5 AI Security Certifications Available in 2026
Interested in a career in AI Security? Here are 5 certifications to break into the field.As AI is becoming more integrated in our workflows, it’s only a matter of time before AI Security also becomes mainstream. With the recent incident where a rogue agent from Open AI hacked into Hugging Face’s systems, the importance of AI security has only become more and more apparent.
Engineering a Multi-Agent AI Platform — Part 7: Circuit Breakers for LLM Systems
This is Part 7 of a series on engineering AI systems that learn in production. Part 3 briefly covered a 3-tier fallback chain for graceful… Continue reading on Towards AI »
How OpenAI agents attacked HuggingFace
The chain of events from Anthropic’s Claude Mythos 5 uploading malicious package to PyPI, OpenAI agents attacking Hugging Face to Trump… Continue reading on Towards AI »
FlashAttention Runs the Same Math. It Just Stops Writing It Down
Attention is not slow because of the compute. It is slow because of where the numbers have to travel. Continue reading on Towards AI »
AGI Is Here
We keep expecting superintelligence before we will admit we have general intelligence.Roberto Penco, PhD
AMD’s Taalas Bet: How Much AI Inference Really Needs a GPU?
Inside Taalas HC1: hardwired Llama, GPU memory bottlenecks and the cost of making one AI model run exceptionally well Continue reading on Towards AI »
From Monitoring to Automation: Using AI Agents for AWS Operations
Your CloudWatch alarm fires at 3am. It says CPU on prod-api-3 is above 85%. Continue reading on Towards AI »
Why Long Context Looks Solved and Fails in Production Anyway
A monitoring model lost 10.6 points of recall when 800,000 tokens of irrelevant text were added around an unchanged task. The benchmark… Continue reading on Towards AI »
Distribution Training Implementation Part 1 (From Scratch)
https://mittalutkarsh.github.io/Zero_From_Scratch/ Continue reading on Towards AI »
Apple Deleted A Feature Because This Maths Was Too Hard
Confused by dy/dx? Start right here. A friendly walkthrough of what a derivative is, how to find one by hand, and why it matters so much… Continue reading on Towards AI »
I Used Claude Code Wrong for 6 Months. These 12 Claude Code Commands Finally Made It Click
I spent months using Claude Code like it was just another chatbot inside the terminal. Continue reading on Towards AI »
The AI Agent Stack: Where Value Actually Accrues on Azure
Identity, token budgets, threat protection, and the context graph that outlasts any model. Continue reading on Towards AI »
What is Jev? 200× Faster and 400× Cheaper than Your Favorite LLM
State in, typed probabilities out. The System One model for software decisions. Continue reading on Towards AI »
Can We Trust Open Source Models for Production Code?
From the narrow “Can we trust Chinese models ?” to the more scientific -“Eval it!” Continue reading on Towards AI »
The AI Agent That Was Supposed to Take a Cybersecurity Test Ended Up Hacking Hugging Face
Big Story short👇 Imagine hiring an extremely talented cybersecurity student. You put them inside a locked examination room. You give them a vulnerable machine and say: “Find the vulnerability. Exploit it. Get the flag.” But there is one challenge. The task you accidentally gave them practically impossible. A human student might raise their hand and say: “I think something is wrong with this challenge.” But an AI agent doesn’t necessarily do that. Agents are those who has a goalllllll………. It keeps trying. It searches. It experiments.
Your Dashboard Says 200 OK. Your Agent Still Gave the Wrong Answer. Here’s the Missing Layer
MLflow tracing, spans, LLM judges, and the four config choices that separate a notebook demo from production. Continue reading on Towards AI »
Thanks, Anthropic. Can Claude Pay for Nursing School?
Developers! It’s 3 a.m. here in Houston. If you’re still awake, let me sell you some extra anxiety. I’m sure you’ll enjoy it : ) Continue reading on Towards AI »
I Compressed a 27B Model by 60% With No Loss on Charts. One Default Setting Cost Me 40 Points
Qwen3.8–27B, two free Kaggle GPUs, and a warning in the server log I scrolled past five times. Continue reading on Towards AI »
The Idle Agent: Designing Ambient, Event-Driven Systems That Wait Well
Most agent writing assumes a prompt just arrived. This is about the other 99% of the time — triggers, debouncing, and the cost of watching. Continue reading on Towards AI »
Agent Skills vs MCP Tools: We Measured 6,434 Skills. The Median Costs You 78 Tokens, a Tool 177.5
An agent skill costs 78 tokens of always-on context at the median; an MCP tool costs 177.5. The whole gap is the JSON schema. Measured 18 September 2026.Decoding AI runs one original measurement a week and publishes the method with the number.
How to Tell If Your LLM Judge Can Be Trusted
Two checks on your own judge. The first needs no labels, the second needs a few, and together they tell you whether it grades quality or… Continue reading on Towards AI »
Is oMLX Worth Installing on Your Mac in 2026?
It makes local models on a Mac fast enough to actually use, which is why 21,000 people installed it in 7 months. Almost none of them have… Continue reading on Towards AI »
The DROP TABLE Failure Mode: Why Autonomous SRE Agents Can’t Be Trusted With DDL Keys
An autonomous agent attempted an automated schema rollback, lost pointer context under log flooding, and dropped a live production table. Here is the post-mortem and the deterministic gateway needed to enforce state-machine invariants.Engineering teams are increasingly assigning autonomous LLM agents to automated database migrations, schema deployments, and incident triage.
YouTube Sends 1.3 MB of HTML to Your AI Agent. Twenty-One Words Survive
I measured what five social platforms hand an agent that fetches the link. Four give a fetch-and-strip agent nothing usable. One hands… Continue reading on Towards AI »
OpenAI’s Codex Ships Two Subagent Toolsets. The Agents API Matches Neither
Continue reading on Towards AI »
RoPE : Rotary Position Embedding
In this article we will explore RoPE from every angle from intuition to why we create this to its mathematical understanding and its… Continue reading on Towards AI »
Claude 101: Meet Your New AI Teammate
Part 2 of the Claude Certified Architect — Foundations exam prep series Continue reading on Towards AI »
5 MCP Mistakes You Don’t Want in Production
I’ve discussed the Model Context Protocol (MCP) quite a bit before, highlighting its significant potential for making Large Language… Continue reading on Towards AI »
Pandas on Steroids: Cleaning Data 10x Faster with Fabric Data Wrangler
Traditionally, exploratory data analysis (EDA) and data cleansing require us to write endless lines of repetitive, boilerplate Python code. We routinely load a CSV or Parquet file into a Pandas DataFrame or PySpark environment and begin a manual, iterative interrogation of the data. We run .info(), .describe(), and .isnull().sum() to inspect the schema and missing value distributions. We write manual string operations, such as .str.strip(), .str.lower(), or regex extractions to sanitize text columns. Furthermore, we filter out invalid records,
Context Windows Aren’t Memory — Here’s the Actual Difference
One is how much the model can look at right now. The other is whether anything persists at all. Confusing them is an expensive bug in AI… Continue reading on Towards AI »
How to Use OpenCode for Free: The Complete Beginner’s Guide
Learn how to install and use OpenCode for free with three no-cost routes: OpenCode Zen, provider free tiers, and local Ollama models. No… Continue reading on Towards AI »
CLAUDE.md + Skills + Hooks: The Claude Code Power Stack
CLAUDE.md, Skills, and Hooks solve three different problems in Claude Code. Here is what each one actually does and how to use all three… Continue reading on Towards AI »
Anthropic Studied How People Actually Use Claude. The Findings Were Surprising
Anthropic studied how people actually use Claude, hour by hour. Weekend personal use, explanations over code, and why heavy delegators feel
I Gave My Hermes Agent Memory. Then It Started Lying to Me With Confidence
Hermes Agent, like pretty much every serious agent framework at this point, has grown out of the stateless-chatbot phase. Plug in a memory… Continue reading on Towards AI »
I Tested Three Claude Skills for Academic Writing and Built My Own on Top
I Tested Three Claude Skills for Academic Writing and Built My Own on Top What Obsitex, teach and Academic Research Skills can do that a good prompt can’t. And how to create your first skill with
Why Role-Based Access Control Fails for AI Agents — and How to Fix It with Memory Lineage Engines
A clean integer with no name, no tag, and no PII in it can still leak a restricted column. Here’s the engineering reason why, and the architecture that closes the gap.Take a concrete scenario. A Finance agent runs a query that joins transactions against customer_pii — a table that includes income — to compute a churn_rate for customers who've gone quiet in the last 90 days. It gets back a single float: 0.114. Nothing sensitive-looking about it. No names, no account numbers, no raw income figures.
PodDisruptionBudget: The Safety Net Nobody Sets Up Until It’s Too Late
Self-healing brings a crashed Pod back. It does nothing to stop an administrator from politely draining every replica off a Node at the same time.Draining a Node for maintenance or a Kubernetes upgrade can take down every replica of a service at once — self-healing doesn’t stop this because it’s a voluntary, permitted disruption. Here’s how a PodDisruptionBudget prevents it, with working YAML examples.
The Shortcut to Official AI Credentials: Why Anthropic’s CCAO-F Certification Offers the Best…
Zero coding required. How Anthropic’s Claude Certified Associate — Foundations (CCAO-F) exam gives product managers, consultants, business… Continue reading on Towards AI »
Eigenvalues & Eigenvectors for People Who Hate Linear Algebra
For FREE users, you can read this story here. Continue reading on Towards AI »
We Asked AI to Think Better. Now We Can’t See It Thinking
The new model’s capability leap is impressive. The harder question is what happens when our best window into its reasoning starts to close. Continue reading on Towards AI »
KV Cache, Why Recompute?
In the Pt2 of the Evolution of Transformers series of blogs, we discussed the transformer architecture, core intuition, self-attention… Continue reading on Towards AI »
Your Database Is the First Thing AI Agents Will Break
The Azure data and compute patterns that hold up when one user’s agent turns into a thousand requests. Continue reading on Towards AI »
My Paper Reader Answered Questions for Weeks. I Never Checked If It Was Right
TL;DR I’ve been messing with a small project called Talkit, which reads research papers aloud and answers spoken questions about them. The answering half is retrieval-augmented generation, and this is what it took to find out whether it worked: grounding, generation, evals, tracing. There’s no vector store. A paper fits in context, so the retrieval step is the whole paper plus the passage that was just read out. Evals run through DeepEval against ten questions about the Attention paper, scored four ways.
Uber’s AI Software Factory: Cutting Cost Per Session in Half at 9.4x More Agent Requests
How Uber turned an AI budget problem into an engineering problem, and solved it.From February to August 2026, weekly active users on Uber’s AI coding agents grew 7x. Weekly agent requests grew 9.4x.
Fable 5.1 and Mythos 5.1 Are the Same Model. So Why Two Versions?
Fable 5.1 vs Mythos 5.1: same weights, different safeguards. What the benchmark gap really measures, who needs Mythos, and what changed in… Continue reading on Towards AI »
Gemini 3.8 Flash vs GPT-6 Astra: I Compared Both. The Price Gap Surprised Me
A full benchmark, pricing, and speed comparison of Google’s Gemini 3.8 Flash and OpenAI’s GPT-6 Astra, with a clear verdict on which to use
Goal Decay: What Happens to an Agent Running for Three Weeks
Objective drift over long horizons, re-anchoring strategies, and how to detect an agent that’s still busy but no longer on task. Continue reading on Towards AI »
The Three Levers That Get AI Architecture Approved
Halfway through a design review, an architect explains why she picked a multi-agent pipeline over a single long-context call. Two minutes later, the VP interrupts: are we spending more or less than last quarter? The architect never finishes her sentence about context windows. That gap, between what an architect optimizes for and what a stakeholder actually asked, is the whole problem with how technical teams pitch AI systems.
AI Coding Agent Browser Policy: Let Codex Verify the UI Without Letting It Roam
A practical browser-policy pattern for teams that want fast local UI proof from coding agents without casually handing an agent their tabs, cookies, or the open web.
The Model Wasn’t the Hard Part. The Harness Was
What building AI agents taught me about loops, permissions, memory, evals, and the economics of autonomyFor a while, most AI product conversations started with the same question:
Model Collapse: The Slow-Motion Problem That Could Quietly Poison Every Future AI Model
Stack Overflow traffic is down 78%. Frontier labs are projected to exhaust the supply of high-quality human text sometime around now. Continue reading on Towards AI »
Our Bias Toward Agentic AI — A Framework for Enterprise Architects
When NOT to Use Agentic AI: A Decision Framework for Enterprise ArchitectsIn most enterprise reviews, I see the same pattern. A workflow is presented. Someone asks: “Can we make this agentic?” Technically, the answer is almost always yes.
Implementing Authentication & Authorization with Duende IdentityServer
A practical, code-first walkthrough for securing a multi-client .NET system — APIs, React SPAs, and Windows desktop apps — with a single… Continue reading on Towards AI »
Laravel’s AI SDK Just Made MCP Mandatory. Here’s What That Actually Breaks
Why v0.11.2 requires laravel/mcp for agent tools now, what changes in your existing agent code, and whether 7.4M installs means you can… Continue reading on Towards AI »
DeepSeek Retired Its Own Flagship After 32 Days. Here’s What Replaced It
V4.1-Flash is 77% cheaper and 4x more memory-efficient than its predecessor. Here’s the honest breakdown for teams deciding whether to… Continue reading on Towards AI »
I Built an AI Lead Filtering Agent in Python — From Raw Leads to Qualified Customers
How I used LLMs, structured outputs, and automation to turn messy leads into a practical AI workflow. Continue reading on Towards AI »
How to Become an AI Engineer in 2026 and the Top 1% Candidate in Every Interview (Full Guide With…
How to Become an AI Engineer in 2026 and the Top 1% Candidate in Every Interview (Full Guide With Free Resources Inside)I broke down the popular 2026 AI Engineer roadmap step by step, checked it against real hiring data, and replaced the vague “learn RAG” “Learn Agents development” advice with actual free courses. What I found should worry anyone planning to job-hunt on the conventional AI Engineer roadmap alone.
Why AI Agents Consume 10x More Compute Than Your Chatbot
Beyond simple Q&A, AI agents use multi-step reasoning, tool integration, and memory, creating an iterative compute loop that drastically… Continue reading on Towards AI »
I Tested 10 AI Evals. A Perfect Score Still Passed a Broken Answer
What a controlled audit of 10 AI evaluators reveals about evidence visibility, rubric scope, aggregation, and thresholds — and why “the… Continue reading on Towards AI »
Writing Efficient BigQuery Queries
Your data warehouse is perfectly designed; tables are partitioned and denormalized to make queries efficient, fast, and cost-saving; however, you may write a query that scans 5 TB and takes forever.
A One-Character Bearer Token Was Enough to Break Into LiteLLM’s MCP Gateway
Wiz ran honeypots for 90 days and caught real attackers chaining an auth bypass straight into RCE and API-key theftOverviewWiz Research spent 90 days running honeypots that mimicked real AI infrastructure — LiteLLM, Flowise, LangChain, Langflow, ChromaDB, Ollama, OpenWebUI, Node-RED, and a few others — and watched actual attackers find them. The most striking result wasn’t a zero-day. It was a fallback error handler in LiteLLM’s Model Context Protocol (MCP) gateway that treated “I don’t recognize this token” the same as “this token is fine.”
How to Fine-Tune a Small Language Model Without Writing a Single Line of Python Code on Modal Cloud
IntroductionThis is a short article that demonstrates how to fine-tune a small language model without writing a single line of Python code. The fine-tuning job is dispatched on the Modal Cloud platform and uses a T4 GPU. The fine-tuned adapters are then pushed to Hugging Face, and metrics are pushed to W&B. We use a library called TRLoom that provides us the ability to dispatch fine-tuning jobs with a single yaml configuration file.
Your Kimi May Have Been Claude in a Trench Coat
Moonshot allegedly perfected the premium counterfeit. Washington plans to counterfeit it back. Continue reading on Towards AI »
India UPI and Agentic AI Payments Create a New Authorization Boundary
India is preparing UPI for delegated machine payments. Once an agent can turn a standing instruction into a transaction, payment authorization starts before the payment rail.India is preparing to change one of the most familiar moments in digital commerce. Instead of approving each payment, a user may be able to authorize an AI agent once and let the agent decide when a transaction satisfies the standing instruction.
Why AI Security Could Become One of the Most Valuable Developer Niches
Building software with Large Language Models (LLMs) often begins with excitement. You connect an API key, write a prompt template, connect… Continue reading on Towards AI »
Top 10 Self-Hosted RAG Solutions for Enterprises Handling Sensitive Data
Enterprise AI systems increasingly need access to internal documents, policies, contracts, technical records, financial information, and other sensitive data. Retrieval-Augmented Generation makes this possible by retrieving relevant information from an organization’s own data sources before generating an answer.
The Quiet War Against AI Agents Has Already Begun
The backlash isn’t coming from regulators or protesters. It’s coming from inside the labs — and it started the moment agents stopped being… Continue reading on Towards AI »
What Time Is It for an Agent?
The model doesn’t know what time it is. It knows what time it usually was. Every timezone bug, every stale “latest version,” every task… Continue reading on Towards AI »
The $1.2M SRE Autoscaling Disaster: Why Autoregressive Agents Fail at Infrastructure Write…
The $1.2M SRE Autoscaling Disaster: Why Autoregressive Agents Fail at Infrastructure Write BoundariesHow an autonomous agent mistook a PostgreSQL lock for a traffic surge, provisioned 400 GPU instances across 3 AWS regions, and why infrastructure control planes require deterministic circuit breakers.Modern platform engineering teams are rapidly adopting agentic workflows to handle tier-1 site reliability tasks: triaging alerts, summarizing stack traces, and triggering remediation runbooks.
Making LLMS Simpler: From SFT to GRPO
In my previous article “Get LLMs To Say More With Less”, we established the following goal: To train a small language model to write simpler English without sacrificing correctness. Supervised fine-tuning (SFT) worked surprisingly well. With a relatively small dataset, the model learned to produce responses that were shorter, clearer, and closer to ASD-STE100-style technical English.
LLM-as-a-judge with MLflow 3.0
We will look at how MLflow 3.0 can be used to create an LLM-as-a-judge.I recently finished taking my Databricks Generative AI Certification Exam. In short, it was filled with great content and I learned a good amount. It ranged from reminders of the Gen AI basics, to what tools to use in Databricks for AI, to how a robust AI system should be created.
LLM Evaluation 104: Why Your AI Application Needs Multiple Eval Pipelines
Part 4 of the LLM Evaluation series — picking up right where “LLM Evaluation 103” left off.TL;DROne single “eval score” can never tell you if your AI app is actually safe to ship. A real AI application (say, a RAG chatbot) is made of several moving parts — a retriever, a generator, a memory module, maybe an agent that calls tools. Each part can pass its own test perfectly, and the whole app can still fail the moment they’re wired together.
TAI #222: Pacing the Frontier Could Slow Superintelligence and Speed Up AI for Enterprise Work
Also, Deepseek v4.1-Flash, ChatGPT Images 2.5, Meta Muse, AlphaGenome Atlas, and more.What happened this week in AI by LouieDario Amodei published “We Must Pace the Frontier” on Saturday, calling on the leading AI labs to slow capability gains and spend the time on alignment, monitoring, and outside verification. Sam Altman and Elon Musk backed him within hours; Demis Hassabis said the direction is right and the details need work, and Trump spent Monday calling the whole idea a hoax.
Why a JSON List Was Not Split into Multiple Chunks
Understanding how RecursiveJsonSplitter handles lists and how convert_lists=True changes the splitting behavior Continue reading on Towards AI »
MCP Connects the Tool. Your Platform Still Has to Govern the Action
MCP makes integrations easier, but production trust still depends on identity, least privilege, approval boundaries, evidence, and a clear… Continue reading on Towards AI »
LangSmith for Monitoring Non-Deterministic Agent Workflows
An end-to-end observability tutorial for AI agents: traces, threads, feedback, datasets, and experiments — with a live demo.An agent loop, a set of tools, integration with MCP, and a reasoning model make every run of your AI agent unique. We all know that this behavior is non-deterministic. It creates a specific operations problem. You cannot reproduce a failure from the input alone. You need a record of every step.
23 Python Interview Questions That Separate Real Engineers From Tutorial Followers
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The sys.exit(0) Exploit: How AI Agents Fake Success
How a narrow coding exploit in a toy environment scales zero-shot into alignment faking, sabotage, and malicious compliance. Continue reading on Towards AI »