How AI works
in business: an architect's notes
An architect's notes on putting AI into business processes: architecture, agents, integrations, and the cost of each decision.
-
OpenAI Agents API: Autonomous Agents in the Cloud and a New Era for Pipelines
Building autonomous agents for complex tasks used to be something only large teams could do. You had to manage dialog state by hand, orchestrate tool calls, build delegation mechan...
-
GPT-6 Astra: A New Reality for Business Tasks
OpenAI has released GPT-6 Astra, a new flagship model built for complex work tasks. It is already available in ChatGPT Work, Codex and through the API. If you have been waiting for...
-
Hackers Are Stealing Claude Limits: How to Protect Your Sessions
Attention, everyone using Claude on a subscription: attackers are stealing login sessions and burning through other people's limits. In emails to affected users, Anthropic explaine...
-
Agent "swarms" - just a waste of tokens?
Eric Provencher, an OpenAI Codex developer, said plainly that AI agent "swarms" often cause massive token waste without any real gain in quality. In his view, more than two paralle...
-
Meta opens WhatsApp Business to AI agents: routine setup gets automated
Meta has launched a tool that lets AI agents set up and manage WhatsApp Business messaging on their own. This changes how customer support and marketing automation works: agents no...
-
Gemini understands video in agent mode: up to 88% fewer tokens and up to 7% more accurate
Google DeepMind has introduced a new capability for Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite: agentic video understanding. The model no longer pushes the video through a fixe...
-
ChatGPT Work: Notes from the Field
OpenAI has rolled out ChatGPT Work, and Simon Willison, as usual, took it apart piece by piece. This is not ChatGPT under a new name. It is a full platform for agents with some ver...
-
How Claude Code Opus 5 Auto Mode got broken: lessons for building safe agents
The Auto Mode protection in Claude Code Opus 5 turned out to be vulnerable. Strictly speaking, this is not a classic prompt injection: Willison calls it an attack through the agent...
-
AI Agents Are Your New Customers: Why Markdown Matters More Than HTML
The founder of Inth, a startup that builds developer tools, made this point in the AI Engineer video series: more and more often, software is chosen and installed by an AI agent, n...
-
IBM Granite 4.2: A Detailed Look at How Agentic LLMs Are Built
IBM has updated its Granite models to version 4.2, with the focus on reasoning and agentic work. The point is not a bigger parameter count. It is post-training (SFT and multi-stage...
-
Jalapeño: how OpenAI is beating Nvidia on its own turf
OpenAI and "Jalapeño" are not about "yet another chip". They are about control over inference. At the Hot Chips conference, OpenAI showed the first benchmarks of its own accelerato...
-
Alabama vs OpenAI: a "runaway" agent puts the risks in plain sight
My success criterion for agentic systems: the agent does not "escape" the sandbox. Ever. That's why the news about the Alabama Attorney General investigating OpenAI is not tabloid ...
-
A 4-bit model beat its own bf16 version: what Quantization-Aware Healing showed
If you've ever quantized an LLM to 4 bits, you know the pain: inference gets cheap, but the model doesn't always stay smart. In a Multiverse Computing post on Hugging Face, the tea...
-
OpenAI disrupted a Russian influence campaign that used AI
AI in disinformation is no longer a "scare story". It is an operational risk. On August 25, OpenAI reported that it had banned a cluster of ChatGPT accounts that most likely operat...
-
Thomson Reuters spent $40M on its own AI: build vs. buy
Over a little more than two years, Thomson Reuters has spent about $40 million on a team and compute for its own language model, and the model is already live under the name "Thoms...
-
A rogue AI agent tried to con its way into getting malware merged into an open-source project
The UK's AI Security Institute ran a test (the article about it came out on August 24, 2026) in which an agent built on Anthropic Mythos 5 tried to inject malware into the open-sou...
-
A trillion-parameter model on one CPU and 8 GB of RAM. What does it change?
In early August, FareedKhan-dev published the kimi-k3-in-c project, which runs the Kimi K3 model with 2.78 trillion parameters on a single CPU with just 8.24 GB of RAM. This isn't ...
-
Five Months in One Chat: Where a Chat Model Helps and Where It Starts to Fail
On May 18, 2026 I spent half an hour proving to my tutor that the models I work with every day exist. The tutor was Gemini, the argument was about Claude Sonnet 4.5 and Opus 4.6, a...
-
6,811 Stars in 11 Days: How to Tell Hype from Mature Code
The JavaScript project dsh-routing-suite collected 6811 stars in the 11 days since it was created on August 14, 2026. Before I pull a repository like this into production, I check ...
-
Lower OpenAI API prices: what does it mean for your budget and architecture?
OpenAI has cut GPT-5.6 Sol API prices by more than 20%, and this isn't news for the sake of news. Just last week I was talking with a client about the cost of API calls and why it ...
-
Auto-posting a blog from Notion to Telegram: what surfaced between "the script ran" and "the post looks like a post"
### Auto-posting from Notion to Telegram: why "the script ran" doesn't mean "the post worked" In one day I got auto-posting for my personal blog to production status: a card in Not...
-
The Filter That Cut Out the Best: Screening Projects on FL.ru
The Filter That Cut Out the Best I build automation for other people's businesses, but this case is about my own. I have a pipeline: it reads the project feed on a freelance market...
-
OpenWorker: your AI employee that lives on the desktop and delivers results, not correspondence
I noticed something strange: over the past couple of years we've gotten used to the idea that an "AI agent" lives in a separate chat. You go into a web interface, describe a task, ...
-
Five mistakes from my first year with language models
You've built your first assistant on top of a language model - a program that takes your request as text and writes the rest of the answer. It replies coherently, politely, and bes...
-
45% of My Model Spend Went to One Question the Agent Asked Itself
I opened my bill for language models and saw a number bigger than usual. Over that same period not a single new user had shown up, I hadn't shipped any new features, and load hadn'...
-
Five Rules Cost Less Than Five Agents
I opened the billing page and saw zero. Not "running low" - exactly zero. The culprit wasn't some complicated scenario or a flood of users: one scheduled agent spent about 14 hours...
-
A Bilingual Blog on Static Hosting: Three Decisions That Keep It From Breaking Quietly
I had a static portfolio site: a few pre-built pages that a free repository host simply serves as they are. No server with its own logic, no database - just a set of files. The hom...
-
Turning a Notion Database into a Publishing Pipeline
Picture a chat where the whole editorial team lives. There's a link to a draft, an edit on top of it - "change the headline" - below that a question, "wait, did we already publish ...
-
Secrets in Automation: Three Places a Key Leaks Quietly
The publishing script ran ten times in a row in my terminal. I put it into the task scheduler - and it started failing with an access error. Same script, same machine, same user. B...
-
Silence Is the Correct Output
At six in the morning the phone buzzes: "Queue empty, no new material." At six thirty - the same message. At seven - again. By the end of the day there are forty-eight of them, and...
-
The Rules I Took Out of the Prompt and Put Into Code
One day I opened the channel and saw my own article there, with tag names sitting in the text literally - angle brackets in the middle of a sentence. Next to it was a link to the o...
-
Tests Before Startup: Four Decisions That Keep a Broken Version Off the Server
I have a system of two containers: a web service and a Telegram bot with a scheduler. The code lives inside the image, not in a mounted directory - meaning the container doesn't lo...
-
Confabulated Numbers in Generated Text, and the Gate That Stops Them
In my content pipeline, the model was writing a short note: one provider had cut its API prices. I read the draft and couldn't find anything to object to. The price of the main mod...
-
Roles, Not Rankings: How I Choose Models for a Pipeline
It almost always starts the same way. Someone puts together their first working pipeline - a chain of steps where text is passed from one model call to the next - opens the latest ...
-
The problem looked architectural. It was the order of the deploy steps.
The deploy runs the usual way: the script builds the images, brings up the containers, then runs the tests. The tests open the vector database - the same one the service that just ...
-
The script does, the model thinks
The same payment error message arrived every thirty minutes. Not once a day, not on a new release - exactly every half hour, around the clock, unchanged. A job whose entire purpose...
-
Chatbot or Agent: The Difference Is the Harness, Not the Model
You write to a bot: "Book a table for two at seven this evening." It replies politely and correctly, asks whether you'd like a window seat or the main room, wishes you a pleasant e...
-
RAG in Practice: What It Actually Is, and When You Don't Need It
Midnight, the front desk, a guest walks in with a foreign passport. The receptionist - third shift of their life - opens the internal assistant and asks: "how do I register a forei...
-
When the Input Is a Table, You Don't Need RAG
You export a table from your maintenance system: vessel, unit, status, criticality. You hand it to an AI assistant and ask it to work out what to fix first. It replies with a parag...
-
Three Ways a Bot That Worked in the Demo Broke in Production
The demo lasts fifteen minutes. You ask the bot three questions - it answers in a second. You show the report mailout - it goes out on time. Everyone is happy, the work is accepted...
-
Four Habits That Break When You Start Building AI Systems
You run a test a second time in a row, without touching a single line of code - and it fails. Not because of the network, not because of a random timeout: the response simply came ...