One AI piece, walked all the way through — newest first. ← Back to the Reading Desk
Some AI-infrastructure startups reach $10M+ in annual revenue within six months, and the essay's answer for how isn't a better product. It's who they sell to: the handful of AI-native companies operat
What changes when a company stops treating AI agents as a tool you open in the code editor and starts wiring them into every job — engineering, data, sales, support? One software company published six
Can an AI improve the very scaffolding that runs it — the search strategy, the memory, the self-checks — without a human tuning any of it? A research team claims the first measured 'yes': a system tha
The competitive edge in AI has moved twice. First it was the model, then the harness wrapped around the model, and now the argument is that it is neither — it is the data you compound on top of both.
There is a classic problem in the market for information: a seller must reveal the idea to sell it, and once revealed it has been given away free. The argument here is that AI flips that risk onto the
The piece builds a working AI from the ground up on one thesis: everything becomes a vector, and learning is nudging numbers until the machine stops being wrong. It walks the full chain in order — emb
The argument: users judge an app in about a second, and most AI-built apps fail that gut-call because they look AI-built — default fonts, the same purple gradient, spacing slightly off. That is a desi
The Claude Code team's own post pins down what an agent 'loop' is and sorts loops into four types by which piece of your judgment you hand off: the check (turn-based), the stop-condition (goal-based),
One argument doing the rounds is that writing a full spec up front never really worked — waterfall lost to agile because teams discover what they actually need while building, not in a document writte
The thing that makes an AI agent genuinely useful over months isn't the model or the tools — it's how you file what it remembers. The argument here is that durable agent memory is an infrastructure pr
The argument is that the current jump in AI quality is not coming from smarter models, it is coming from wrapping a model call in a loop: generate, evaluate, learn, improve, repeat. The piece sorts 20
An agent kept burning nearly two minutes opening the wrong files to fetch a brief written three months earlier, and the fix wasn't a better model. The same wandering happened on Opus, GPT 5.5, Qwen an
A company brain — a shared, AI-readable memory of how an organisation actually works — lives or dies on adoption, not architecture. Drawn from 200-plus deployments, the piece argues the tech barely de
The piece argues that out-of-the-box Claude Code uses a fraction of its capability, and the rest lives in open-source add-ons that bolt on memory, discipline and agent teams. Most of the hyped repos a
Most enterprise AI pilots fail for one reason: they hand employees yet another tool to open, and adoption stalls at 25-30%. The fix the piece proposes is to stop selling an interface and start selling
Lindy, a general-purpose AI assistant, moved most of its traffic off Claude and Gemini onto DeepSeek v4 Flash and cut the per-use model cost on those routes by about 90%. The real argument is that the
The post is a 20-item list of what a production AI engineer should actually know, and its argument is that the job moved off 'writing good prompts' onto engineering the reliable system around the mode
The piece argues you forget most of what you read because reading feels like understanding when it's only recognition, and the fix is to retrieve the idea rather than re-read it. It packages Richard F
Stop asking which coding AI is best and ask which one fits which job. One argument doing the rounds, after weeks of daily use, is that Claude Code (with Opus) and Codex (with GPT-5.5) have opposite st
The piece repackages two old, well-evidenced decision tools — Gary Klein's premortem (HBR 2007) and NASA's pre-launch Failure Review Board and Go/No-Go poll — into four copy-paste Claude prompts you r
Most corporate AI stalls at the pilot stage for one blunt reason: nobody can say how good the AI actually is. The argument doing the rounds is an 'eval-first' mentality — treat your scored test suite
Almost every retrieval system inherits one unexamined assumption: that a chunk of text is the right thing to embed. The argument here is that this single choice causes most retrieval failures — the on
A viral post claims a whole suite of finance tools — DCF and LBO models, equity research, KYC, NAV tie-outs — was just open-sourced as a free plugin, and that bank analysts are about to be replaced. T
Autonomous coding has shifted from 'better prompting' to 'better control systems' — the argument doing the rounds is that you wrap a model in a goal, an evaluator, a verifier, and a loop so it keeps w