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.
38 posts
- LangChain’s xAI update is really about model contracts LangChain’s langchain-xai 1.3.0 release looks small, but the interesting bit is the direction: provider wrappers are moving from thin adapters toward stricter model contracts, with reasoning effort, streaming chunks, tracing metadata, and profile drift checks becoming part of the developer surface.
- OpenAI’s small business push is really a workflow bet OpenAI’s ChatGPT for Small Businesses program is less about one new feature than a bet that owners will pay for practical AI habits: writing, admin cleanup, customer replies, and repeatable automations inside ChatGPT Work.
- Stack Overflow’s AI problem is the missing feedback loop A Hacker News graph framed Stack Overflow as an AI casualty, but the deeper issue is not fewer pageviews or questions. It is that programming knowledge used to compound in public, and AI tools now answer privately unless builders design a new loop.
- ChatGPT’s computer control turns the browser into the new agent runtime OpenAI’s new ChatGPT app points at a practical shift: agents are moving from chat responses into browser tabs, desktop apps, and background workflows. The useful question is not whether this feels magical, but where it saves real operator time without creating new review burdens.
- Open AI is a stack problem, not a license argument The open source AI debate keeps collapsing code, weights, data, training recipes, and usage rights into one phrase. For builders, the useful question is narrower: which parts of the stack can you inspect, modify, run, and trust?
- Shopify’s agent lesson is delegation, not autonomy Shopify’s ChatGPT Work example is less about magic agents and more about a managerial shift: non-engineers delegating bounded operations tasks, building small internal tools, and removing queue time, while measurement and governance remain the part OpenAI’s customer story does not answer.
- Virgin Atlantic’s ChatGPT story is really about workflow compression OpenAI’s Virgin Atlantic example is less about a magic chatbot and more about compression: dashboards, reporting, prioritization, and product loops moving faster when business teams can prototype against messy internal data with AI in the middle. The hard part is governance.
- Grok Build’s Apache 2.0 release puts the boring work on users Grok Build being open sourced under Apache 2.0 is useful, but the license is only the start. The real test is whether builders can inspect it, run it, adapt it, and trust it in workflows that survive outside the demo.
- New model releases do not reset the advantage Hugging Face’s “Newer Models, Same Advantage” points at a pattern builders keep relearning: absolute model quality improves, but relative gaps often survive unless you change the task, data, workflow, or product surface that created the gap in the first place.
- Claude for Teachers is really a feedback-loop product Anthropic’s Claude for Teachers pitches a practical shift from one-off lesson prompts to recurring classroom workflows that combine transcripts, assessments, standards, and teacher review. The useful part is not magic lesson planning. It is feedback loops, if schools handle privacy, data quality, and professional judgment carefully.
- ChatGPT onboarding starts with a real task, not a perfect prompt OpenAI’s beginner framing for ChatGPT is simple: start a conversation, then use it for writing, brainstorming, and problem solving. The practical lesson is narrower and more useful: new users should bring a real task, ask for a draft, and learn to steer the system.
- “Ask an LLM” Is Not an Answer to Every Question The reflex to send every question to ChatGPT misses what people are often asking for: judgment, context, trust, and a human read on messy tradeoffs. LLMs are great first-pass tools. They are not a replacement for expertise or community.
- Intelligence Is Still Not the Product The AI 2040 framing points at a real trap: treating intelligence as the main variable. For builders, the harder problem is turning model capability into systems that work under cost, latency, trust, workflow, and organizational constraints.
- Same-prompt app builds are useful, but not enough A Hacker News comparison of GPT-5.6, Grok 4.5, Claude, and Muse Spark building the same four apps points at a better way to judge coding models: less leaderboard watching, more repeatable product-shaped tests.
- Leanstral 1.5 points at proofs as a workflow, not a stunt Mistral’s Leanstral 1.5 announcement is thin on public detail here, but the direction is clear: formal proof models are moving from research theater toward practical loops for math, code, and specification work.
- TPG’s ChatGPT rollout targets the boring costs of diligence OpenAI’s TPG case study is light on hard metrics, but the useful signal is clear: private equity teams are using ChatGPT less as an oracle and more as a cheaper first pass for market research, spreadsheet work, and data-room triage.
- Timestamp drift is the quiet ASR failure that breaks real workflows The REDDIT ASR paper is a useful reminder that transcripts can look right while their timestamps are wrong, and that fixing a narrow model behavior without wrecking the rest of the system is often the real engineering problem.
- A GPT-5.6 rumor matters only if Codex changes with it A thin claim says GPT-5.6 Sol Ultra is headed for Codex. The useful question is not whether the name is real, but whether OpenAI is treating coding agents as the proving ground for frontier models.
- An aviation headline in the AI feed is a pipeline problem A Delta firework incident near Midway is not an AI story, which makes it useful anyway. It exposes a basic failure mode in automated feeds: bad classification can waste attention before a model ever gets involved.
- Agentic coding works best when the repo has rails Agentic coding is less about a model magically becoming a staff engineer and more about giving a capable loop tight boundaries, testable tasks, and enough repo context to recover when it goes wrong.
- The Model Got Smarter and My Tool Got Dumber As frontier models improve, the tools wrapped around them often get worse: more scaffolding, more prompt overhead, more guardrails fighting the very capability you paid for. A look at why that happens and how builders can avoid it.
- Buccy Bench makes model progress look usefully stupid Matt Wolfe’s SVG benchmark asks models to draw Gary Buucy with code, which sounds like a joke because it is. The useful part is what it exposes: multimodal taste, code precision, cost, latency, and the weird gap between passing tests and making something recognizable.
- Code-as-image is a cost hack, not a free lunch A reported 60% Fable cost cut from sending code as images points to a strange new optimization: model pricing can make the cheapest representation different from the most natural one, but OCR-based code review has sharp edges builders need to measure before trusting it.
- Local LLMs are becoming a workflow choice, not a hobby project Jamesob’s local LLM guide is a useful signal: the question is no longer whether serious models can run off-cloud, but where local inference beats hosted APIs on privacy, iteration speed, cost control, operational simplicity, and real builder workflows today without pretending the frontier has moved onto your laptop.
- Anthropic’s Python SDK points to agents as infrastructure, not demos Anthropic’s latest Python SDK releases add claude-sonnet-5 support, but the more useful signal is managed-agent plumbing: streaming deltas, scoped credentials, overrides, pagination, and webhooks. That is the boring layer builders need before agent products can survive contact with production.
- Hermes Agent moves the agent demo into the payments layer Hermes Agent’s new Stripe and Nvidia integrations point to a practical shift: agents are no longer just chat loops, they are being wired into payments, servers, sandboxes, and product workflows, which makes permissioning and operational design more important than model theater.
- Hermes turns agent setup into packaging work Matthew Berman’s Hermes walkthrough shows a small but important shift in agent products: the hard part is no longer only model access or chat UI. It is packaging skills, hosting, credentials, and workflow defaults so a non-specialist can actually start using the system.
- Local AI is insurance, not a bunker Alex Finn’s local AI argument mixes useful operator instincts with some shaky crisis framing. The practical takeaway is narrower and better: run local models for privacy, cost control, offline workflows, and continuity, not because every frontier model is about to disappear behind a government velvet rope.
- CUGA's Two Dozen Examples Are the Real Agent Documentation Hugging Face shipped CUGA, a lightweight agent harness with about two dozen working examples, and the examples matter more than the framework. Here is what builders should actually copy and where the thin claims still hide.
- What llama.cpp's commit log tells us about AI-written code Two back-to-back llama.cpp releases ship a new speech model and a WebGPU speedup, and the commit metadata quietly reveals how much of this code an AI agent now writes per stage.
- Entity gap patching: the pSEO maintenance loop most teams skip A working note on using LLM entity extraction to keep programmatic SEO pages competitive without rewriting them. The job isn't generating more pages, it's patching semantic gaps in the pages you already have before rankings decay.
- Cursor is the spreadsheet moment for marketing ops Coding agents like Cursor have crossed a threshold where a marketer with no engineering background can build the scrapers, reporting tools, and automation glue they used to wait months for. Here is what that actually changes for a digital marketing operator in 2026.
- Replacing the gated PDF with a working tool built in an afternoon Conversational app builders like Lovable change the economics of lead magnets. Instead of another ebook nobody reads, a marketer can ship a working calculator or audit tool by Friday. Here is how I would actually run that play, and where it quietly falls apart.
- Marketers are still vibe-checking prompts. Frontier devs run evals before lunch. Frontier developers test new models with automated eval suites and track agent success rates as a percentage. Most marketers eyeball outputs and call it good. Here's how to port the eval mindset into a content or SEO workflow without a research team.
- Stop Vibe-Checking New Models. Build a 50-Prompt Eval Set Instead. Frontier developers run automated evals the moment a new model lands. Most marketers still open the chat window and eyeball the output. Here's how to close that gap with a simple benchmark set of past briefs, ad copy, and SEO tasks you can rerun in an afternoon.
- The mechanism matters: making AI competitive analysis auditable Single-shot prompts produce competitive analyses that look right but can't be trusted. The fix is treating the workflow itself as the artifact, with a logged, replayable trail of how the answer was built.
- Why I Stopped Trusting My Own Prompts (And Started Logging Them) Most marketers treat prompts like throwaway text. The ones getting real output are treating them like code, with versions, evals, and a quiet discipline about what actually moved the needle.
- Why I Stopped Trusting AI Demos and Started Timing My Own Workflows Most AI tool demos collapse the moment you put them against a stopwatch and a real client deliverable. Here is how I now evaluate whether a tool actually saves time in a marketing workflow, and why most of them quietly cost more than they save.