Building With AI
I build with AI in public: agents, automations, and web systems, shipped and documented, including the pipeline that writes this site. This hub is the running log. Not tutorials in the abstract, but what actually worked, what broke, and the boring infrastructure that decided the outcome. The through-line: shipping with AI is less about the model and more about the plumbing, the taste, and the willingness to cut what isn't working. If you'd rather see the receipts than the highlight reel, start here.
92 posts
Recent articles (30)
- Browser agents are becoming practical comment analysts Marketing AI Institute’s LinkedIn comment workflow shows where browser-based AI agents already help: fast qualitative sorting, theme detection, and follow-up planning. The catch is that sentiment is not truth, and operators still need sampling, source checks, and human judgment.
- OpenAI’s Python SDK gets key expiration controls, not just new image hooks OpenAI’s September Python SDK releases add GPT Image 2.5 support on the client side and, more importantly for operators, service account key expiration fields and controls. The useful move is not hype around images. It is making API credentials easier to govern.
- Pick the hospital automation work before picking the bot A healthcare RPA framework argues for ranking automation candidates before buying tools, with process taxonomy, suitability scoring, tier selection, and ROI modeling. The useful lesson is not the exact healthcare numbers. It is the operating discipline.
- Procedural Graphs give agents a memory of what to do, not just what happened A new arXiv paper proposes storing agent know-how as a graph of procedures instead of a growing chat history, and lets an LLM edit that graph from its own wins and losses. Here is what that changes for anyone shipping long-horizon agents.
- What ChatGPT Images 2.5 Changes for People Who Actually Ship Images OpenAI announced ChatGPT Images 2.5 with better handling of sketches and reference photos, but the details that matter to working teams are still thin. Here is what is confirmed, what is not, and how to test it before you trust it.
- OpenAI’s journalism program stretches from classrooms to newsrooms OpenAI’s journalism expansion is less about one media partnership and more about shaping the whole talent and production pipeline. The useful question is not whether AI belongs in journalism, but where it improves reporting without blurring accountability.
- OpenAI's Python SDK Gets More Honest About Agent Failures OpenAI’s Python SDK v3.9.0 is a small release, but it points at a bigger shift: production AI apps now need cache visibility, retry sanity, and cleaner event handling more than another demo.
- Agent Harnesses Are Becoming a Token Efficiency Fight A Reddit benchmark comparing Claude Managed Agents, LangChain DeepAgents, and TrueForge points to a practical question for builders: not which agent feels smartest, but which runtime gets the same work done with fewer tokens, fewer tool calls, and less operational drag.
- Local LLMs as the first responder for a compromised PC A messy r/LocalLLaMA incident points to a practical use for small local models: not replacing security tools, but helping a panicked user reason through containment, evidence, and next steps while offline.
- The useful part of calling LLMs a cognitive virus A Hacker News framing of LLMs as a cognitive virus is too broad if read as moral panic, but useful if builders treat model output as something that can silently replicate assumptions, errors, and style through real workflows.
- The real lesson in a 90% Claude Code token cut A Hacker News item credits Portal by Spotify with a 90% Claude Code token reduction. The useful takeaway is not the exact number, it is that coding agents need better context boundaries before they need bigger models.
- What LangChain Core 1.6.2 Fixes for Agent Builders A close read of the langchain-core 1.6.2 release: async tool support for OpenAI, two content-mutation bugs squashed in the standard content layer, and what those small fixes actually mean for people running agents in production.
- Repo-distilled skills are the missing middle layer for research agents The arXiv paper Repo-To-Skill argues that agents need compact operational knowledge, not just bigger models or longer context, and reports sizable benchmark gains from turning ML repositories into verified reusable skills.
- ACToR targets the tokens where repo-level code generation breaks ACToR reframes code retrieval as a timing problem: fetch repository context at the few generation points where a wrong token sends the model down the wrong path, not only before generation starts.
- AI workflows should create new work, not just faster tasks Liza Adams argues that marketing teams miss the bigger AI opportunity when they only speed up existing work. The useful shift is designing workflows that make previously impractical analysis, personalization, and iteration possible.
- Culture Still Sets the Ceiling on AI Productivity AI can speed up local tasks, but team culture decides whether that speed turns into shipped work or just faster churn, duplicated effort, and prettier confusion.
- No AI Fridays: What a Weekly Ban Reveals About Skill Atrophy A team experiments with banning AI coding tools one day a week to keep their engineers sharp. The idea is simple, the evidence is thin, and the underlying worry about skill atrophy is worth taking seriously anyway.
- When LLM memory becomes program analysis A Hacker News post with a revealing title points at a useful pattern: when an AI coding system stores structured observations about code, tests, files, and edits, memory becomes less like chat history and more like a lightweight program analysis layer.
- MCR-Bench shows code review agents still lose the plot MCR-Bench reframes AI code review as a multi-round state-tracking problem, not a one-shot bug hunt. That matters because real pull requests change over time, and the reported failures point straight at memory, workflow design, and evaluation gaps.
- Anthropic’s Python SDK is tightening the agent plumbing Anthropic’s latest Python SDK releases are not headline model news, but they matter for builders running agents against real files, cloud transports, webhooks, org controls, and changing event streams.
- Gemini Omni 1.1 Flash needs to prove control, not just speed Google DeepMind is positioning Gemini Omni 1.1 Flash around builder control, but the useful question is narrower: what control surfaces actually change production behavior, cost, latency, safety, and debugging for teams shipping AI workflows?
- Codex at loveholidays points to the real no-code shift OpenAI’s loveholidays Codex story is less about replacing engineers than moving small software bets closer to the teams that feel the pain. The useful question is how companies govern that flow without turning every prototype into production debt.
- OpenAI’s full-stack argument is really an economics argument Sarah Friar’s “The full stack behind abundant intelligence” frames OpenAI’s advantage as compounding progress across chips, compute, models, and products. The useful question for builders is whether that stack turns into cheaper, more dependable task completion.
- Prime Agent treats the harness as part of the model Prime Agent’s reported ARC-AGI-3 jump is eye-catching, but the useful idea is narrower: long-horizon agents need persistent execution, memory, recovery, verification, and inspectable subagents before their model capability can be measured fairly.
- Compliance LLMs need different workflows for passports and DPIAs A compliance paper suggests LLMs can help assemble EU regulatory artifacts, but only when teams match the workflow to the artifact: schemas and validators for strict product passports, richer context, evidence collection, and human review for open-ended privacy assessments.
- Codex over Claude is a workflow signal, not a verdict A Hacker News discussion about using Codex more than Claude is useful less as a model ranking and more as a reminder: coding assistants win when they fit the operator’s loop, repo habits, and tolerance for review.
- Munder Difflin and the real work behind AI clone offices A Hacker News listing frames Munder Difflin as an agent harness for running an office of clones. The useful question is not whether clones sound like you, but whether the harness can route work, preserve context, and fail safely.
- Claudette and the fight against AI house style A small Hacker News item points at a bigger operator problem: AI writing tools keep drifting toward the same glossy voice, and teams need stricter style systems, not vibes.
- What LangChain's perplexity 1.4.1 patch says about agent plumbing A tiny LangChain point release for its Perplexity integration is mostly bug fixes, but the specific fixes reveal how much agent reliability now depends on unglamorous plumbing like Responses API item types and preserved request parameters.
- AI literacy is the new fault line: what OpenAI's CodeAI deal actually signals OpenAI is betting on students, not just researchers, while quieter arXiv work shows AI moving from demos to deployed underwriting and unlabeled biology. Together they map where applied AI actually stands and what a builder should do about it now.
Earlier articles (61)
- Gemini 3.7 Flash and the New Floor for Cheap Models
- AI coding feels like managing a very literal junior engineer
- Alzheimer’s surgery claims need evidence before amplification
- A one-line Hacker News item is not enough context
- What Actually Makes a Claude Code Session Productive
- What OpenAI's v3.1.0 SDK Changelog Tells Us About Its Roadmap
- OlmoEarth embeddings are useful if they survive outside the studio
- RingCentral’s AI-native work story is really about the handoff
- VICBench shows vulnerability detection still needs humans
- AI security review is hitting Bitcoin repos, not just toy code
- Claude’s Bluetooth hint is the right kind of AI assistance
- AI coding cost control is an engineering workflow problem
- Kitesurf and the browser built for agents, not humans
- LLMs as semantic scouts for compiler optimizations
- OpenAI’s education plugins move ChatGPT closer to classroom workflow
- The Financial Advice Chatbots Give You Depends on the Question You Bring
- AI reasoning can look right while taking shortcuts
- Flint points at a missing layer in AI visualization
- Go’s generic collections proposal is really about shared defaults
- The useful question behind a multiplayer agent harness
- RL can teach code models to care about runtime, but the stopwatch is the hard part
- Brolly’s plain-text weather page is an AI product lesson
- Half-Life 2 on HaikuOS and the AI runtime tax
- LangChain’s xAI update is really about model contracts
- OpenAI’s small business push is really a workflow bet
- Stack Overflow’s AI problem is the missing feedback loop
- ChatGPT’s computer control turns the browser into the new agent runtime
- Open AI is a stack problem, not a license argument
- Shopify’s agent lesson is delegation, not autonomy
- Virgin Atlantic’s ChatGPT story is really about workflow compression
- Grok Build’s Apache 2.0 release puts the boring work on users
- New model releases do not reset the advantage
- Claude for Teachers is really a feedback-loop product
- ChatGPT onboarding starts with a real task, not a perfect prompt
- “Ask an LLM” Is Not an Answer to Every Question
- Intelligence Is Still Not the Product
- Same-prompt app builds are useful, but not enough
- Leanstral 1.5 points at proofs as a workflow, not a stunt
- TPG’s ChatGPT rollout targets the boring costs of diligence
- Timestamp drift is the quiet ASR failure that breaks real workflows
- A GPT-5.6 rumor matters only if Codex changes with it
- An aviation headline in the AI feed is a pipeline problem
- Agentic coding works best when the repo has rails
- The Model Got Smarter and My Tool Got Dumber
- Buccy Bench makes model progress look usefully stupid
- Code-as-image is a cost hack, not a free lunch
- Local LLMs are becoming a workflow choice, not a hobby project
- Anthropic’s Python SDK points to agents as infrastructure, not demos
- Hermes Agent moves the agent demo into the payments layer
- Hermes turns agent setup into packaging work
- Local AI is insurance, not a bunker
- CUGA's Two Dozen Examples Are the Real Agent Documentation
- What llama.cpp's commit log tells us about AI-written code
- Entity gap patching: the pSEO maintenance loop most teams skip
- Cursor is the spreadsheet moment for marketing ops
- Replacing the gated PDF with a working tool built in an afternoon
- Marketers are still vibe-checking prompts. Frontier devs run evals before lunch.
- Stop Vibe-Checking New Models. Build a 50-Prompt Eval Set Instead.
- The mechanism matters: making AI competitive analysis auditable
- Why I Stopped Trusting My Own Prompts (And Started Logging Them)
- Why I Stopped Trusting AI Demos and Started Timing My Own Workflows