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Programming & AI: Tips 💡 Articles 📕 Resources 👾 Design Patterns 💎 Software Principles ✅ 🇳🇱 Contact: @MoienTajik 🎯 Buy ads: https://telega.io/c/ProgrammingTip
Claude Haiku 5.5 is available on the Claude Platform 🚀
Anthropic released Claude Haiku 5.5, its lower-cost small model, on the Claude Platform.
The model ID is claude-haiku-5-5. This is the one to benchmark when your workload has lots of short agent turns: classification, extraction, routing, simple tool calls, and coding tasks where latency matters more than maximum reasoning depth.
What to test first:
• Replace your fast-path model in staging.
• Measure tool-call accuracy, not just chat quality.
• Set a harder-model fallback for failed evaluations.
Small models pay off when they handle the boring 80 percent cleanly. Keep the expensive model for the weird tickets.
[ Read More ] :
https://www.anthropic.com/claude-haiku-5-5
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#AI #LLM #Claude #API
@ProgrammingTip
OpenAI Decisions API is in public beta 🚀
OpenAI's Decisions API is now in public beta. It uses GPT-6 Luna to turn text or image input into structured decision results instead of another blob of prose.
What it returns:
• Predicates: true or false checks for policy, routing, or eligibility.
• Choices: select one option from a defined set.
• Scores: rank or grade an input on a scale you control.
This is useful when an LLM sits inside a real workflow. Send the result to an approval queue, a feature flag, or a C# switch expression. Keep the model's job narrow, validate the typed output, and do not make your app parse "I think yes".
[ Read More ] :
https://developers.openai.com/api/docs/guides/decisions
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#AI #OpenAI #LLM #API
@ProgrammingTip
AI is changing the developer career ladder 🚀
GitHub's latest developer career advice is refreshingly practical: AI can write more of the first draft, but it cannot own the engineering outcome for you.
The skills to double down on:
• Systems thinking: understand the service, data flow, failure modes, and tradeoffs around the code.
• Judgment: spot the plausible-looking AI patch that breaks security, cost, or production behavior.
• Communication: turn a vague product request into constraints an agent, teammate, and reviewer can act on.
Using Copilot or an agent is becoming normal. Being the person who can frame the task, verify the output, and ship it safely is still the hard part.
[ Article ] :
https://github.blog/ai-and-ml/ai-is-rewriting-the-developer-career-ladder-heres-how-to-stand-out/
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#AI #GitHub #Copilot #Career
@ProgrammingTip
Cloudflare shipped an agentic CLI for its API ⚡️
Cloudflare launched cf, an agentic CLI for its full API surface.
This is a practical alternative to collecting one-off curl commands in a wiki or maintaining small admin scripts for every service. The CLI gives humans and coding agents one command-line entry point for Cloudflare operations.
Where it fits ✅:
• Inspect and change Cloudflare resources while debugging a service.
• Give an agent a constrained operational interface instead of raw dashboard access.
• Turn repeatable incident steps into checked-in commands and runbooks.
Do not hand an agent broad production credentials because it has a nice CLI. Use scoped tokens, separate environments, and audit the resulting changes.
[ Blog ] :
https://blog.cloudflare.com/cloudflare-cf-cli-launch
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#Cloudflare #CLI #DevOps #Agents #LLM
@ProgrammingTip
Claude Sonnet 5.5 is built for coding agents 🚀
Anthropic released Claude Sonnet 5.5 for the Claude Platform. It targets the work developers actually hand to coding agents: navigating a repo, making changes across files, using tools, and checking the result.
What changed:
• Faster agentic work: aimed at shorter tool loops and less idle time while an agent investigates a codebase.
• Lower-cost option: positioned for teams that need to run coding tasks repeatedly, not just ask one-off questions.
• Production focus: Anthropic calls out software engineering and multi-step agent workflows directly.
If your agent spends more time calling tools than writing code, model latency and per-task cost matter as much as benchmark scores. Test it on a real issue queue, with your actual tool permissions.
[ Read More ] :
https://www.anthropic.com/claude-sonnet-5-5
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#AI #LLM #Claude #CodingAgents
@ProgrammingTip
Claude Opus 5.5 is now on the Claude Platform 🚀
Anthropic introduced Claude Opus 5.5, a lower-cost frontier model available in Claude Code and through the Claude Platform.
That matters if your agent workload has been split between a high-end model for hard tasks and cheaper models for everything else. A lower-cost Opus tier can change where that handoff happens.
What to check:
• Run your existing eval set, not a few cherry-picked prompts.
• Measure tool-call accuracy and recovery after a failed call.
• Compare total agent cost: tokens, retries, and human review time.
For Claude Code users, model choice is now a practical repo-level config decision, not just a benchmark chart.
[ Blog ] :
https://www.anthropic.com/claude-opus-5-5
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#AI #LLM #Claude #ClaudeCode
@ProgrammingTip
Hex turns GPT-6 Astra analysis into visual reports 🚀
Hex is using GPT-6 Astra to turn complex analysis into visual reports. The useful bit is not just asking a model to summarize a table. It is moving from an analysis request to a result people can inspect and share.
What this points to:
• Analysis as an artifact: teams need charts, assumptions, and outputs, not a chat answer pasted into Slack.
• Human review still matters: a clean report can hide bad joins, stale data, or a wrong metric definition.
• Tool context is the product: models get more useful when they operate inside the workspace where data and business logic already live.
For AI app builders, this is the bar: produce a result that can survive review, not just a plausible paragraph.
[ Read More ] :
https://openai.com/index/hex-gpt-6-astra
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#AI #LLM #GPT6 #Data
@ProgrammingTip
GitHub improved the Copilot code review flow 🔥
GitHub shipped an improved Copilot code review experience. That matters most on the boring, high-volume PRs where reviewers need help finding a real issue, not another summary of changed files.
For C# teams, use Copilot review as an extra pass on every ASP.NET, EF Core, and library PR. Then keep the human review focused on contracts, failure behavior, and whether the change belongs in the codebase.
Practical rule:
• Let Copilot flag suspicious diffs.
• Do not merge because it found nothing.
• Keep analyzers and tests as the enforcement layer.
AI review is cheap coverage. It is not a replacement for someone who knows why that nullable property or cancellation token exists.
[ Read More ] :
https://github.blog/changelog/2026-09-18-copilot-code-review-an-improved-review-experience
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#dotnet #csharp #GitHub #Copilot
@ProgrammingTip
GitHub moved the Copilot runtime to Rust 🚀
GitHub migrated the runtime behind Copilot to Rust, and used Copilot during the migration itself.
✅ Why this is worth reading:
• Runtime work is systems work: a language migration means tracing real production behavior, not just translating syntax.
• Copilot was part of the workflow: useful for exploring an unfamiliar codebase, drafting changes, and keeping momentum through repetitive conversion work.
• The output still needs engineering judgment: boundaries, performance, correctness, and rollout safety are not autocomplete problems.
This is a practical case study for teams asking where coding agents help on a large refactor. Use them to accelerate investigation and implementation. Keep humans on architecture, tests, and production checks.
[ Blog ] :
https://github.blog/ai-and-ml/generative-ai/migrating-the-github-copilot-runtime-to-rust-using-copilot
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#AI #GitHub #Rust #Copilot
@ProgrammingTip
Make coding agents earn the green check 💡
Do not end an agent task with "implement this." End it with a command that can fail.
For a .NET repo, that might be dotnet test, a focused test project, a formatter check, or a small reproduction script. Tell the agent which command to run, what output to inspect, and what it should do if the command fails.
A good task contract has three parts:
• Change: the files or behavior to update.
• Proof: the exact command or test case.
• Stop condition: what needs human review instead of another retry.
This also makes PR review faster. You get a diff plus evidence, not a confident paragraph saying the fix should work.
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#AI #dotnet #Testing
@ProgrammingTip
Agents API is in public beta 🚀
OpenAI put the Agents API in public beta. Cloud agents with the Codex harness, managed sessions, tools, and either a hosted sandbox or one you bring yourself.
What shipped:
✅ Codex harness, not a chat-completions wrapper you have to babysit
✅ Managed sessions and tools in the API
✅ Hosted sandboxes, or bring your own
Public beta, so expect sharp edges. If you ship coding agents or anything that needs a real environment, this is the post to read. Not another model card. Call it if you are done stitching sessions, tools, and a VM by hand.
[ Read More ] :
https://openai.com/index/introducing-the-agents-api/
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#AI #LLM #Agents
@ProgrammingTip
Anthropic just introduced Claude Fable 5 and Claude Mythos 5 🚀
The interesting part:
Fable 5 and Mythos 5 are basically the same underlying model, but with different access and safety layers.
✅ Claude Fable 5 is the version available for general users. Anthropic says it is their most capable generally available model so far, especially for:
• Software Engineering
• Long-running agentic tasks
• Vision-based work
• Knowledge work
• Scientific reasoning
• Long-context workflows
One example they shared is pretty wild:
Stripe tested it on a 50M-line Ruby codebase, and it completed a migration in one day that would normally take a team more than two months. 🤯
But because the model is much stronger, Anthropic is adding stricter safeguards.
For risky areas like cybersecurity, biology/chemistry, and model distillation, some requests will automatically fall back to Claude Opus 4.8 instead of Fable 5. ⚠️
Claude Mythos 5 is the restricted version, mainly for trusted Cyber defenders, infrastructure providers, and later selected biology researchers. It has some safeguards lifted depending on the access program.
Pricing is 💸:
• $10 / 1M input tokens
• $50 / 1M output tokens
[ Read More ] : https://www.anthropic.com/news/claude-fable-5-mythos-5
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#AI #Claude #LLM
@ProgrammingTip
🚀 .NET 10 is here! 🔥
The successor to .NET 9 is now out — and it’s a Long-Term Support (LTS) release, supported for the next 3 years.
👉 [ Learn what’s new ] :
https://learn.microsoft.com/en-us/dotnet/core/whats-new/dotnet-10/overview
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#dotnet #csharp #aspnetcore #maui
@ProgrammingTip
🚀 OpenAI just dropped Sora 2 — and it’s insane.
Every frame of the video you’ll see is fully generated by AI. No cameras, no actors, no sets. Just prompts → video!
[ Blog ] : https://openai.com/index/sora-2
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#AI #OpenAI #Sora
@ProgrammingTip
Announcing ChromeDevTools MCP! 🔥
Connect your AI coding agent to Chrome's powerful automation & debugging capabilities with ease.
Key features:
✅ Reliable automation: It can programmatically handle clicks, form fills, dialogs, and page navigation with ease.
✅ Performance insights: Go beyond simple audits. Instruct your agent to record a performance trace and extract actionable insights to optimize your web apps.
✅ Advanced debugging: Empower your AI to analyze network requests, list console messages, take screenshots, and even evaluate scripts in the browser context.
✅ Browser emulation: Easily test different conditions by emulating CPU slowdowns, network throttling, or various screen sizes.
Works well with modern web apps and believe this will unlock new workflows for automated testing, AI-driven debugging, and interactive web development.
[ Blog ] : https://developer.chrome.com/blog/chrome-devtools-mcp
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#AI #LLM #MCP
@ProgrammingTip
GPT-6 and Intelligent UI are rolling out 🚀
OpenAI announced GPT-6 for everyone, alongside its new Intelligent UI direction.
This is worth watching even if you only ship API-backed apps. Model upgrades change what users expect from a product UI: fewer settings screens, more intent-driven actions, and interfaces that can adapt while the user works.
For developers:
• Keep your product actions explicit and observable.
• Make AI suggestions reversible, not magic state changes.
• Do not hide latency behind a clever UI. Show progress and let users interrupt.
The model is one part. The interaction contract around it is where apps get hard.
[ Read More ] :
https://openai.com/index/gpt-6-for-everyone/
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#AI #OpenAI #GPT6
@ProgrammingTip
Cloudflare shipped a Web Search API ⚡️
Cloudflare introduced a Web Search API. Search can now be a service call in the app stack instead of a pile of scraped HTML, brittle selectors, and browser automation. 🏳️🌈
[ Read More ] :
https://developers.cloudflare.com/changelog/post/2026-10-02-introducing-web-search-api
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#AI #Cloudflare #Agents
@ProgrammingTip
GPT-6 Astra gets an Ultrafast API tier 🚀
OpenAI added an Ultrafast speed tier for GPT-6 Astra in Codex and the API.
The important bit is real-time work. Astra can now run Responses API workflows over WebSockets, which is a much better fit for interactive coding assistants, live agent status, and UI flows where waiting on a full request feels bad.
What to check:
• Responses API: use it for the agent loop and tool calls.
• WebSockets: keep one live connection instead of polling.
• Ultrafast tier: test it where latency matters more than squeezing every last token of quality.
If your app streams agent work to a browser, this is worth benchmarking against your current model setup.
[ Article ] :
https://community.openai.com/t/build-ultrafast-with-astra-in-codex-and-the-api/1402393
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#AI #OpenAI #API #LLM
@ProgrammingTip
OpenAI introduced Dots, always-on agents 🚀
OpenAI introduced Dots, its take on always-on agents.
This is not a one-prompt, one-answer workflow. The pitch is an agent that can stay active around work instead of waiting for you to reopen a chat and restate the task.
Why this matters:
• Long-running work: agents need durable context, not a pile of copied prompts.
• Real handoff points: a useful agent should surface decisions and results, not silently keep doing things.
• Agent ops: permissions, logs, cancellation, and cost limits become product features.
If you build agent workflows, the hard part is no longer getting a model to call a tool. It is making an autonomous process observable enough that somebody will trust it.
[ Read More ] :
https://openai.com/index/introducing-dots/
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#AI #Agents #OpenAI
@ProgrammingTip
Claude Opus 5.5 is available in the API 🚀
Anthropic released Claude Opus 5.5, and it is available through the Claude API.
The practical pitch is simple: lower typical token costs than Opus 5, plus a faster mode when response time matters more than squeezing out the last bit of reasoning.
What to check in your evals:
• Run the same tool-use and coding tasks against Opus 5.
• Measure latency separately for normal and faster mode.
• Track input and output tokens, not just the model's listed price.
A cheaper high-end model changes agent architecture decisions. Some workflows that needed routing to a smaller model may now fit under one stronger default.
[ Read More ] :
https://www.anthropic.com/claude/opus
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#AI #LLM #Claude #API
@ProgrammingTip
GPT-6 Sol and GPT-6 Luna are in the API 🚀
OpenAI shipped GPT-6 Sol and GPT-6 Luna for API developers, alongside their availability in Codex and ChatGPT.
This is a two-model release, so do not blindly swap your existing production model. Put both behind the same eval set first: tool calls, structured output, long-context retrieval, refusal behavior, and latency under your real prompt size.
What to do this week:
• Add Sol and Luna as versioned model options in your config.
• Run replay traffic against a fixed golden set.
• Log model ID, token use, tool errors, and task success separately.
A model migration is an engineering change, not a dropdown change.
[ Read More ] :
https://openai.com/index/introducing-gpt-6-sol-and-luna/
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#AI #OpenAI #LLM #API
@ProgrammingTip
Cloudflare saved 100 TB of RAM with math and Rust 🔥
Cloudflare reclaimed more than 100 TB of RAM globally in a Pingora-based consistent-hashing service. No new hardware. The win came from changing data representation and the algorithms around it.
✅ Lessons:
• Measure retained memory, not only allocation rate.
• Check collection shape: duplicated keys, oversized buckets, and pointer-heavy graphs add up fast.
• Fix the model first: a smaller or more compact structure usually beats micro-optimizing a hot loop.
For high-cardinality caches, routing tables, or tenant maps, take a heap dump before reaching for another cache node. A few bytes per entry becomes expensive at fleet scale.
[ Blog ] :
https://blog.cloudflare.com/saving-100-tb-of-ram-with-math
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#Performance #Memory #Rust
@ProgrammingTip
Gemini 3.8 Live is generally available 🚀
Google moved Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking to general availability in the Gemini API.
These are the real-time audio models exposed through the Live API. Live is for the voice path: streaming audio in, streaming audio out, and handling a conversation without bolting together STT, an LLM call, and TTS yourself.
What to check:
• Gemini 3.8 Live: the lower-latency voice model.
• Extended Thinking: use it when the spoken request needs more reasoning before the reply.
• GA status: worth revisiting if you held off on a preview-only voice feature.
For .NET teams, this is a good fit for a streaming WebSocket service, not a request-response controller.
[ Read More ] :
https://ai.google.dev/gemini-api/docs/changelog
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#AI #Gemini #LLM #API
@ProgrammingTip
.NET 11 performance work is landing across the stack 🔥
Microsoft published the running list of performance improvements headed for .NET 11. This is not one magic runtime switch. The work spans the runtime, JIT, libraries, and tooling paths that show up in normal application code.
Use the post as a migration checklist:
• Find code that is CPU-bound, allocation-heavy, or called per request.
• Run your benchmarks on the .NET 11 SDK, not a synthetic microbenchmark only.
• Check p50 and p99 latency, allocations, startup, and throughput separately.
The best upgrade wins are boring: existing C# gets faster with fewer code changes. But measure your service. A JIT win can disappear behind JSON, EF queries, network calls, or a container CPU limit.
[ Blog ] :
https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-11/
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#dotnet #csharp #Performance #dotnet11
@ProgrammingTip
GPT-6 Astra is OpenAI's new work model 🚀
OpenAI shipped GPT-6 Astra. They are calling it the next generation in intelligence for work.
Not a research teaser. A work model. Expect the name in agent stacks and coding tools.
Why you care:
✅ New flagship name to put on your eval harness
✅ Pitched for workplace tasks, not playground chat
✅ If you pin model ids in config, read this before you swap anything
Start with the official writeup. Then decide if your coding agents still belong on last week's snapshot. I would not change production on a blog title alone. Run your own traces.
[ Read More ] :
https://openai.com/index/gpt-6-astra-next-generation-work
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#AI #LLM #OpenAI
@ProgrammingTip
GPT-Live-1 is in the OpenAI API 🚀
OpenAI put GPT-Live-1 in the API. Out of preview. You can call it.
Same full-duplex voice ChatGPT already uses. It listens and talks at the same time, then hands reasoning and tools to a backend model or agent.
What shipped:
✅ Full-duplex voice sessions you can wire into an app
✅ Pair it with whatever backend model, tools, or agent you already run
✅ Built for interruption and overlap, not a turn-detector kludge
You keep your existing model for thinking. GPT-Live-1 is the ears and mouth.
If you ship voice agents, this is the post. Not the GPT-6 Astra work page.
[ Read More ] :
https://openai.com/index/introducing-gpt-live-1-in-the-api
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#AI #API #LLM
@ProgrammingTip
Cursor agents can now control their own computers 👾
Cursor introduced cloud agents that can control their own computers (VMs) and build software end-to-end.
Instead of just generating code, agents can now:
✅ Run full dev environments in isolated cloud machines
✅ Test features, fix issues, and validate results
✅ Record videos / screenshots / logs as proof
✅ Create merge-ready PRs automatically
At Cursor, 30% of merged PRs are already created by these autonomous agents.
This changes the developer role:
👉 Less micro-coding, more direction, review, and decision-making.
The long-term vision is “self-driving codebases”:
agents that ship features, manage rollouts, and monitor production. 🚀
[ Read More ] : https://cursor.com/blog/agent-computer-use
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#AI #Cursor
@ProgrammingTip
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Preparing for the .NET 10 GC 🔥
In .NET 9 DATAS (Dynamic Adaptation To Application Sizes) got enabled by default, but .NET 9 is not an LTS release, so for many people they will be getting DATAS for the first time when they upgrade to .NET 10.
What does “application size” mean exactly?
This is the LDS (Live Data Size) from GC’s point of view, meaning that if we did the most aggressive GC possible, this is how much memory your application uses. Another way to look at it is this is your long lived data + whatever inflight data you have when a GC occurs.
The goal for DATAS is that you no longer need to do various configurations to try to achieve a heap size proportional to your application usage. The 2 main cases we target with DATAS are:
1) Bursty workloads running in memory constraint environments. DATAS aims to retract the heap size back when the application doesn’t require as much memory and grow it when the app requires more. This is especially important for apps running in containers with memory limits.
2) Small workloads using Server GC — for example, if someone wants to try out a small asp.net core app to see what the experience is like in .NET, DATAS aims provide a heap size much more inline with what the small app actually needs.
[ Article ] : https://maoni0.medium.com/preparing-for-the-net-10-gc-88718b261ef2
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#dotnet #gc
@ProgrammingTip
GitHub MCP Registry: The fastest way to discover MCP Servers 🔝
If you’ve tried connecting AI agents to your development tools, you know the pain: MCP servers scattered across numerous registries, random repos, buried in community threads — making discovery slow and full of friction without a central place to go. Meanwhile, MCP server creators are worn out from publishing to multiple places and answering the same setup questions again and again.
The GitHub MCP Registry launches as your new home base for discovering MCP servers. Whether you’re building with GitHub Copilot, agents, or any AI tool that speaks MCP, this is the place to find what you need. With GitHub already home to most MCP servers, the MCP Registry makes them dramatically easier to discover, explore, and use — helping developers find the right tools faster and contribute to a more open, interoperable ecosystem. ✔️
[ GitHub MCP Registry ] : https://github.com/mcp
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#LLM #AI #MCP #GitHub
@ProgrammingTip