Programming & AI Tips 💡
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Programming & AI: Tips 💡 Articles 📕 AI & LLMs 👾 Design, Architecture, Principles ✅ 🇳🇱 Contact: @MoienTajik
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📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 46 044 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 27 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -412 و در ۲۴ ساعت گذشته برابر -12 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
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📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“Programming & AI:
Tips 💡
Articles 📕
AI & LLMs 👾
Design, Architecture, Principles ✅
🇳🇱 Contact: @MoienTajik”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 28 اکتبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.
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46 045
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
46 045
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
46 045
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
46 045
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
@ProgrammingTip46 045
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
46 045
.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
46 045
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
@ProgrammingTip46 045
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
46 045
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
46 045
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
46 045
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
46 045
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
46 045
🚀 .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
46 045
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46 045
🚀 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!
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#AI #OpenAI #Sora
@ProgrammingTip
46 045
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
46 045
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
46 045
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
46 045
Jetbrains Grazie: AI writing companion for people in tech 🚀
If you spend more time writing documentation, commit messages, blog posts or support tickets than you’d like, JetBrains just launched something you’ll want to try: Grazie.
It’s an AI writing assistant built for people in tech, helping make all those non-code texts better, faster.
🔍 What Does Grazie Do?
• Instant proofreading (grammar, spelling, punctuation, style)
• Inline text completion
• Summarization
• Translation across several languages
• Rephrasing / tone correction / synonymization
• AI-Chat
🛠 Where It Works & Pricing
• Plugin for JetBrains IDEs
• Browser extension for Chromium-based browsers
• Google Docs, Sheets, Gmail
• GitHub
• Slack
• And many more ...
[ Download ] : https://www.jetbrains.com/grazie
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#AI #Jetbrains #GrammarAI
@ProgrammingTip
46 045
🖼 Gemini’s Nano Banana
Google just introduced Nano Banana, a new image-editing feature powered by Imagen 4. 🍌
🔹 Merge multiple photos seamlessly
🔹 Apply style & texture transfers
🔹 Edit backgrounds and objects with natural prompts
🔹 Generate high-quality visuals in seconds
Why it matters:
For developers and designers, Nano Banana is a fast way to prototype visuals, explore UI ideas, and experiment creatively without heavy tools.
You can use Nano Banana directly inside Gemini. 🚀
[ Read More ] : https://gemini.google/overview/image-generation
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#AI #Gemini #Google
@ProgrammingTip
