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Ask the starred-repo corpus questions in natural language: embed the query on-device, rank the stored vectors, and show the matching repos in the TanStack Start UI. This is where the earlier chapters pay off.
Claude Code makes a lot of small judgments that don't need a full LLM call: does this edit break a rule in my CLAUDE.md, which of my installed skills fits this prompt, does this diff need a careful review or a quick pass.
Originally published at erikhill.dev. The numbers below are checked against the repository they come from. My arithmetic grader reads the first number in the sentence, not the answer This is a finding about my own harness. No model got the arithmetic wrong.
I lost two days last quarter reconciling three versions of a CER because someone had downloaded the template, edited it offline, and uploaded it back without checking what had changed in the meantime.
People ask it as a yes or no. It isn't one. Not because nobody knows the answer, but because "job" is the wrong unit to ask about. Almost nobody does one thing at work. You do a bundle of tasks, and AI hits each one differently. Some are already mostly automatable. Some are decades out.
You asked something. The answer came back polished. Later you found out it was wrong. A statistic that doesn't exist. A citation to a paper nobody wrote. A confident summary of a document that says the opposite. The frustrating part isn't the mistake.
If you give an LLM access to your code, you write tools. A tool is a function plus what a model needs to call it: a name, a description, and a schema for the arguments. Then you write the same tools again. The first version uses the AI SDK's tool().
Privacy-Preserving Active Learning for heritage language revitalization programs across multilingual stakeholder groups Introduction: When a Dying Language Met Differential Privacy Last spring, I found myself hunched over a laptop at 2 AM, staring at a dataset of 4,200 annotated sentences in…
The AI CEO is now a product with a waitlist. On September 14, Y Combinator-backed Andon Labs launched Pion, "an agent designed to run any company fully autonomously", with email, phone, banking, a browser and a computer.
When it comes to naming a company which makes autonomous vehicles and AI software, one must think carefully. This kind of technology brings together software, hardware, transport, data and systems and it's important that the name leaves enough room for all of these areas.
Prompt privacy: treat generative AI as a new data-egress channel Generative AI adds a new data-egress path to the workplace: the prompt box. A user can paste a customer complaint, a contract, a spreadsheet or a medical note into an external service in seconds.
Short answer: put one authenticated backend endpoint between your property-management application and an OpenAI-compatible image service, submit a constrained prompt, store the accepted output under an immutable key, and return a durable asset URL plus a request ID. Start synchronously.
The rapidly improving capabilities of AI in mobile development have triggered a new conversation around how mobile teams approach multiplatform development. Notably, Shopify has recently been discussing their move from React Native back to independent native Android and iOS applications.
I ran Laya, an open 421M-parameter decision model, in a browser tab and measured what it costs: download, speed and accuracy. In mid-September, TypeSafe AI released Jev, a “System One” model. It doesn’t write text.
Instantly.ai shows up in nearly every "best cold email tool" list, and for good reason: unlimited sending accounts and built-in warmup on every plan is a genuinely useful pitch. But most reviews stop at the feature list and the pricing page.
The fastest way to connect an AI agent to application data is often a generic SQL tool. Give the model a schema, accept a SQL string, run it, and return the rows. That is also where a prototype can quietly become a production security and maintenance problem.
Rust does not have a shortage of good crates. There is uuid for identifiers, chrono for time, rust_decimal for exact decimal arithmetic, serde_json for JSON, and reqwest for HTTP. The challenge in application development is often not finding a capability.
Most AI writing tools still assume the same workflow: Stop writing Copy the sentence Open a browser tab or side panel Ask for a rewrite Read three versions Paste one back Fix the tone so it sounds like you again That is fine for long documents. It is terrible for everyday writing.
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