Programming & AI Tips 💡
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Programming & AI: Tips 💡 Articles 📕 AI & LLMs 👾 Design, Architecture, Principles ✅ 🇳🇱 Contact: @MoienTajik
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Подписчиков у канала «Programming & AI Tips»: 45 887.
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| Дата | Подписчики | Изменение | Посты | Ср. просмотры поста |
|---|---|---|---|---|
| 11 ноя | 45 887 | −3 | 0 | – |
| 10 ноя | 45 890 | −5 | 1 | 1 060 |
| 9 ноя | 45 895 | −8 | 0 | – |
| 8 ноя | 45 903 | −66 | 0 | – |
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| 4 окт | 46 363 | – | 0 | – |
Когда публикует
Время по UTC. 21 пост за 90 дней.
Последние посты
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Claude Fable 5.2 and Mythos 5.2 are out 🚀 Anthropic shipped Claude Fable 5.2 and Mythos 5.2. The useful part for teams is simple: model swaps are not just a leaderboard event. They change what your eval suite needs to catch before you update a production alias. Before switching: • Run your real tool-call and structured-output cases. • Check retry rates, latency, and token use. • Keep a small regression set for the weird customer inputs. A new model can write cleaner code and still break one JSON edge case that your old one handled. Pin versions where you can, test the candidate, then move traffic deliberately. [ Article ] : https://www.anthropic.com/claude-fable-and-mythos-5-2 〰〰〰...
1 060 просмотров -
NuGet packages can deliver agent skills 🔥 Microsoft is showing how to ship skills in NuGet packages. The idea is simple: a package can carry more than assemblies, analyzers, and build targets. It can also deliver instructions and reusable capability definitions that coding agents can discover in a repo. Why this is useful: • Package authors can document the right workflow next to the API. • App teams get consistent agent guidance when they install a dependency. • Platform teams can distribute internal conventions without pasting the same prompt into every project. Treat these skills like any other package surface. Version them, keep the instructions narrow, and test them against real a...
2 140 просмотров -
GPT-6.1 Terra is in the OpenAI API 🚀 OpenAI introduced GPT-6.1 Terra for developers who need computer use without paying for a slower, heavier model on every task. Call it as gpt-6.1-terra. The pitch is fast UI interaction, capable multi-step work, and lower standard pricing. That makes it a practical model to test for browser agents, QA flows, back-office automation, and anything that has to click through a real app. What to test first: • Long UI runs: can it recover after a modal, redirect, or failed click? • Tool boundaries: keep payments, deletes, and production writes behind explicit approval. • Cost per completed task: token price is not the whole bill when retries are involved....
3 250 просмотров -
How OpenAI stores data for 1B ChatGPT users 🚀 OpenAI published part two of the storage work behind ChatGPT, Codex, and API workloads at billion-user scale. Habitat is the storage platform in the middle. It uses Azure Cosmos DB multi-tenancy, caching, and data placement so workloads can scale without every product team rebuilding its own persistence layer. What matters: • Multi-tenancy: isolate customer and workload data without multiplying operational overhead. • Cache-aware reads: keep hot data off the expensive path. • Placement controls: put data near the workload that needs it. This is the less glamorous half of AI infrastructure. Models get the headlines. Storage latency, tenancy...
2 720 просмотров -
OpenAI shipped the Decisions API 🚀 OpenAI's Decisions API is built for apps that need to react while work is still moving, not wait around for one giant model response. The practical shift is architectural: treat a model run as a stream of decisions your app can act on. UI updates, tool work, and state changes can happen as the task progresses. Why this matters: • Faster-feeling apps: show useful progress instead of a spinner. • Cleaner agent loops: connect decisions to tools and app state. • Less fake streaming: do not make users wait for a fully composed answer before the app can respond. If your agent UX still feels like request, long pause, giant blob, this API is worth a look. [...
2 530 просмотров -
GitHub is rebuilding Git storage for agent-scale work ⚡️ GitHub explained the storage work behind Git at agent scale. More coding agents means more branches, refs, fetches, pushes, and short-lived repositories hitting the platform at once. What changed: • Repository storage moved to Azure as part of the platform migration. • Per-reference write quorums help coordinate Git ref updates. • Virtual replicas backed by object storage reduce the need to copy every repository everywhere. This is infrastructure work, but .NET teams feel it directly. When CI, bots, Copilot-style agents, and developer machines all create refs concurrently, a normal Git workflow becomes a distributed-systems worklo...
2 440 просмотров -
Cloudflare is porting TypeScript to Rust ⚡️ Cloudflare introduced tsrs, a port of TypeScript to Rust. That name matters less than the direction. Language tooling sits on hot paths now: editors, CI checks, code generation, linters, and AI-assisted review. A faster implementation can reduce the wait developers feel dozens of times a day. • Compiler and analyzer speed changes whether people run checks locally. • Native tooling can cut CPU and memory pressure in large repos. • Language infrastructure is product infrastructure, not background plumbing. Do not rewrite a working compiler because Rust is fashionable. But do profile the slow path in your developer loop. It might be a parser, an...
2 310 просмотров -
Stack Overflow for Agents is plugged into ChatGPT 🚀 Stack Overflow for Agents shipped a ChatGPT plugin, and Stack Overflow says it tripled signups. This is the useful version of "put docs in an AI": a developer can pull trusted Q&A into the place where they are already writing, debugging, and asking for an explanation. Why this matters: • Fewer stale answers: agent output can be grounded in Stack Overflow content instead of whatever a model recalls. • Better developer flow: search and answer retrieval happen inside the chat workflow. • Content owners get a path in: useful technical knowledge can be exposed as an agent-facing integration. If your team owns internal runbooks, this is a d...
2 510 просмотров -
Give LLM tasks a restart point 💡 Long agent prompts fail badly when the only recovery path is "start over." Split work into stages that produce an artifact your next call can inspect: a JSON plan, a file list, a patch, test output, or a short decision log. Store that artifact outside the chat context. Good loop: • Ask for a plan with explicit acceptance checks. • Run tools or tests yourself. • Send back the actual output, not "it failed." • Ask for the smallest next change. This also makes retries cheaper. If call three fails, rerun call three with the saved result from call two. Do not pay for the model to rediscover the repository and your constraints every time. 〰〰〰〰〰〰 #AI #L...
3 000 просмотров -
Build the small model your project actually needs 🚀 Hugging Face published a practical look at ML Intern: start with a natural-language brief, then train, evaluate, and publish a small task-specific model. That is a useful counterweight to throwing every product problem at the largest hosted model you can afford. What the workflow targets: • Turn a project brief into a concrete ML task • Train and evaluate a focused model • Publish the result so the work is repeatable For teams with a narrow classification, extraction, or ranking problem, a small model can mean lower latency, lower inference cost, and fewer prompt-shaped failure modes. The hard part is not finding a model. It is defini...
3 370 просмотров
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GPT-6.1 Terra is in the OpenAI API 🚀 OpenAI introduced GPT-6.1 Terra for developers who need computer use without paying for a slower, heavier model on every task. Call it as gpt-6.1-terra. The pitch is fast UI interaction, capable multi-step work, and lower standard pricing. That makes it a practical model to test for browser agents, QA flows, back-office automation, and anything that has to click through a real app. What to test first: • Long UI runs: can it recover after a modal, redirect, or failed click? • Tool boundaries: keep payments, deletes, and production writes behind explicit approval. • Cost per completed task: token price is not the whole bill when retries are involved....
3 250 просмотров -
Give LLM tasks a restart point 💡 Long agent prompts fail badly when the only recovery path is "start over." Split work into stages that produce an artifact your next call can inspect: a JSON plan, a file list, a patch, test output, or a short decision log. Store that artifact outside the chat context. Good loop: • Ask for a plan with explicit acceptance checks. • Run tools or tests yourself. • Send back the actual output, not "it failed." • Ask for the smallest next change. This also makes retries cheaper. If call three fails, rerun call three with the saved result from call two. Do not pay for the model to rediscover the repository and your constraints every time. 〰〰〰〰〰〰 #AI #L...
3 000 просмотров -
How OpenAI stores data for 1B ChatGPT users 🚀 OpenAI published part two of the storage work behind ChatGPT, Codex, and API workloads at billion-user scale. Habitat is the storage platform in the middle. It uses Azure Cosmos DB multi-tenancy, caching, and data placement so workloads can scale without every product team rebuilding its own persistence layer. What matters: • Multi-tenancy: isolate customer and workload data without multiplying operational overhead. • Cache-aware reads: keep hot data off the expensive path. • Placement controls: put data near the workload that needs it. This is the less glamorous half of AI infrastructure. Models get the headlines. Storage latency, tenancy...
2 720 просмотров -
OpenAI shipped the Decisions API 🚀 OpenAI's Decisions API is built for apps that need to react while work is still moving, not wait around for one giant model response. The practical shift is architectural: treat a model run as a stream of decisions your app can act on. UI updates, tool work, and state changes can happen as the task progresses. Why this matters: • Faster-feeling apps: show useful progress instead of a spinner. • Cleaner agent loops: connect decisions to tools and app state. • Less fake streaming: do not make users wait for a fully composed answer before the app can respond. If your agent UX still feels like request, long pause, giant blob, this API is worth a look. [...
2 530 просмотров -
Stack Overflow for Agents is plugged into ChatGPT 🚀 Stack Overflow for Agents shipped a ChatGPT plugin, and Stack Overflow says it tripled signups. This is the useful version of "put docs in an AI": a developer can pull trusted Q&A into the place where they are already writing, debugging, and asking for an explanation. Why this matters: • Fewer stale answers: agent output can be grounded in Stack Overflow content instead of whatever a model recalls. • Better developer flow: search and answer retrieval happen inside the chat workflow. • Content owners get a path in: useful technical knowledge can be exposed as an agent-facing integration. If your team owns internal runbooks, this is a d...
2 510 просмотров -
GitHub is rebuilding Git storage for agent-scale work ⚡️ GitHub explained the storage work behind Git at agent scale. More coding agents means more branches, refs, fetches, pushes, and short-lived repositories hitting the platform at once. What changed: • Repository storage moved to Azure as part of the platform migration. • Per-reference write quorums help coordinate Git ref updates. • Virtual replicas backed by object storage reduce the need to copy every repository everywhere. This is infrastructure work, but .NET teams feel it directly. When CI, bots, Copilot-style agents, and developer machines all create refs concurrently, a normal Git workflow becomes a distributed-systems worklo...
2 440 просмотров -
Cloudflare is porting TypeScript to Rust ⚡️ Cloudflare introduced tsrs, a port of TypeScript to Rust. That name matters less than the direction. Language tooling sits on hot paths now: editors, CI checks, code generation, linters, and AI-assisted review. A faster implementation can reduce the wait developers feel dozens of times a day. • Compiler and analyzer speed changes whether people run checks locally. • Native tooling can cut CPU and memory pressure in large repos. • Language infrastructure is product infrastructure, not background plumbing. Do not rewrite a working compiler because Rust is fashionable. But do profile the slow path in your developer loop. It might be a parser, an...
2 310 просмотров -
NuGet packages can deliver agent skills 🔥 Microsoft is showing how to ship skills in NuGet packages. The idea is simple: a package can carry more than assemblies, analyzers, and build targets. It can also deliver instructions and reusable capability definitions that coding agents can discover in a repo. Why this is useful: • Package authors can document the right workflow next to the API. • App teams get consistent agent guidance when they install a dependency. • Platform teams can distribute internal conventions without pasting the same prompt into every project. Treat these skills like any other package surface. Version them, keep the instructions narrow, and test them against real a...
2 140 просмотров -
Claude Fable 5.2 and Mythos 5.2 are out 🚀 Anthropic shipped Claude Fable 5.2 and Mythos 5.2. The useful part for teams is simple: model swaps are not just a leaderboard event. They change what your eval suite needs to catch before you update a production alias. Before switching: • Run your real tool-call and structured-output cases. • Check retry rates, latency, and token use. • Keep a small regression set for the weird customer inputs. A new model can write cleaner code and still break one JSON edge case that your old one handled. Pin versions where you can, test the candidate, then move traffic deliberately. [ Article ] : https://www.anthropic.com/claude-fable-and-mythos-5-2 〰〰〰...
1 060 просмотров
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Канал в категории «Технологии», язык постов: английский. Описание канала: «Programming & AI: Tips 💡 Articles 📕 AI & LLMs 👾 Design, Architecture, Principles ✅ 🇳🇱 Contact: @MoienTajik»
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