𝗜𝗻𝗖𝗼𝗱𝗲𝗿-𝟯𝟮𝗕: 𝗖𝗼𝗱𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 tackles the persistent gap between impressive general‑purpose code LLMs and the harsh realities of industrial software development, where hardware semantics, specialized language constructs, and tight resource budgets turn many “smart” models into unreliable assistants. Existing models are trained on public repositories that lack the execution‑grounded feedback loops essential for chip design, GPU kernel tuning, embedded firmware, and CAD scripting. Consequently, they falter when asked to respect CUDA grid limits, synthesize Verilog that passes RTL simulation, or generate microcontroller code that boots on real hardware. InCoder‑32B is built to close that divide. The authors train a 32‑billion‑parameter recurrent architecture from scratch using a three‑stage Code‑Flow pipeline: (1) pre‑training on a curated mix of public code and industrial‑grade repositories, augmented with automated verification; (2) mid‑training that progressively expands context windows from 8 K to 128 K tokens with synthetic reasoning trajectories and agentic prompts; and (3) post‑training that grounds the model in execution results across reconstructed industrial environments (Verilog simulation, CUDA A100 execution, STM32 Renode emulation, and OpenCascade CAD). Both an instruction‑tuned and a “thinking” variant emerge, ready to reason step‑by‑step before emitting code. - Achieves 74.8 % pass rate on SWE‑bench Verified, 49.14 % on LiveCodeBench, and 60.99 % on BFCL, matching or surpassing larger proprietary models. - Sets the strongest open‑source baselines on 9 industrial benchmarks covering chip design, GPU kernel optimization, embedded systems, and 3D modeling. - Demonstrates robust handling of hardware constraints, e.g., flattening CUDA grid dimensions to avoid the 65 535 y‑dim limit that trips other models. - Shows that repository‑transition data and mid‑training reasoning trajectories markedly improve performance under distribution shift. - Unlocks emergent “thinking” capabilities that enable the model to plan, verify, and iterate on code before final output. By unifying disparate industrial domains under a single, execution‑aware code model, InCoder‑32B paves the way for trustworthy AI‑assisted engineering—from silicon to shaders to firmware—reducing the manual overhead of low‑level optimization and verification while preserving safety and performance guarantees. #AIforCode #IndustrialAI #LLMResearch

AI coding is gambling: relying on AI to write code can feel like betting on luck rather than logic. Pair AI with strong testing, review, and guardrails to turn risk into reliable results. #AICoding #SoftwareEngineering #AIforCode

**The AI code review revolution is here!** Anthropic just launched Code Review, a tool that uses AI to catch bugs and security risks in code generated by other AI tools, like Claude Code. This is a game-changer for enterprise users, who are seeing a surge in code output and struggling to keep up with pull requests. According to Cat Wu, Anthropic's head of product, Code Review is designed to integrate with GitHub and automatically analyze pull requests, leaving comments on potential issues and suggested fixes. I think this is a crucial step in ensuring the quality and reliability of AI-generated code, and it's a testament to the growing importance of AI in software development. What do you think - will AI code review tools become the new norm in tech? #AIforCode #CodeReview #TechInnovation techcrunch.com/2026/03/09/an…

AI Code Translation: Requirements and Challenges gemini.google.com/share/5d19… #AIforCode #Linux #FutureOfWork #GenAI #CodingAgents

AI Code Translation: Requirements and Challenges #AIforCode gemini.google.com/share/5d19…

github.com/Mohitkumar1322/Co… This is the Link for the Project check out guys and give me feedbacks if any. #ChromeExtension #GitHub #OpenSource #DevTools #AI #Coding #DSA #CompetitiveProgramming #CodeOptimization #DeveloperTools #GitHubAPI #JavaScript #NodeJS #AIForCode #Productivity

Today I almost lost my mind trying to install Google Antigravity on Windows 😂Found 3 key gotchas the hard way: 1️⃣ For login, it works best with a Google Workspace domain account. My personal Gmail kept failing, even with VPN in TUN mode. 2️⃣ Install it directly on the C drive. 3️⃣ Manually point your project directory to: C:\Users\yourname\.gemini\antigravity\playgroundI ignored these at first and got spammed with errors for way too long. Posting this so others don’t waste the same time — and yes, that Reddit reply with the Clinton “biggest winner” meme was absolutely right. #GoogleAntigravity #Gemini3 #AI #GenAI #AIforCode #DevTools #VibeCoding

just read about SWE-Compass, a new benchmark for evaluating large language models in software engineering and I'm LOW KEY EXCITED arxiv.org/abs/2511.05459 #AIforCode #Automation

Tetrix turns your GitHub into a living knowledge graph. Instant search. Smarter gen. Context-aware reviews. 🎥 Watch the demo → youtu.be/iTARwCNLFtU #Tetrix #AIforCode #DevEx #GitHub #BuildInPublic #AItools #Developers

Want your AI to instantly pick up the mood of your design? Instead of saying “Make this look cool,” prompt your model like: 🧠 “Design a retro-futuristic SaaS landing page with vaporwave aesthetics and 90s arcade energy. Heavy on neon gradients, grid lines, and synthwave typefaces.” This is vibe coding. #AItools #webdesign #buildinpublic --- When storytelling meets prompting, bland outputs die fast. Instead of prompting: “Write a product launch post.” Try: “Write a narrative of a founder who nearly gave up, then discovers a hidden insight that turns their side project into a $100k launch. Keep the tone raw, honest—David Perell meets Casey Neistat.” Output: “Four failed launches. A dwindling bank account. Then I noticed something—a live chat user kept typing one word: ‘Why?’ That ‘why’ became a feature. Launched it alone from a cabin. Day 1: $7,400. Day 30: $102k MRR…” This is how you lead AI to emotion. #ContentMarketing #Startups #AIwriting --- Design prompts that demand opinion. If you want AI to act like a design partner, push it beyond visual description. Prompt: “Critique this hero section like a brutal creative director. Prioritize clarity, visual hierarchy, and first-impression stickiness.” This activates its internal design judgment instead of surface-level style. #UIDesign #AIUX #Figma --- The best coders know: modularity isn't just a React thing—it's a prompt superpower. Instead of writing one long system prompt, break it down: 1. Define coding style 2. Explain architectural principles 3. Inject specific project context 4. Then give the task Chunked prompting = cleaner logic, reusable parts. #DevTools #AIforCode --- Prompting for product strategy? Don’t just ask for “growth ideas.” Ask: “What would a strategy consultant at BCG recommend to increase activation rate from 22% to 40% in a freemium SaaS that targets early-stage startups?” You funnel the AI through a sharper mental model. #ProductStrategy #SaaS #AItools --- Productivity frameworks prompt engineering = wild results. Prompt: “Use the PARA system to organize my Notion workspace. Show me how to separate active Projects, Areas of Responsibility, Resources (including swipe files notes), and Archives. Make it usable for someone working in a startup with chaotic priorities.” Output: “→ Projects: Launch V2, Investor pitch, Migration to GCP → Areas: Biz Dev, Growth, Ops → Resources: Copywriting library, Growth experiments, Legal docs → Archives: Sunset experiments, paused hires…” AI’s great at sorting signal from noise if you give it a second brain to work with. #Notion #Productivity #PKM --- 90% of LLM users waste potential by asking for summaries. Flip it. Prompt: “Rewrite this article into a viral tweet thread using Alex Hormozi’s style—high-impact hooks, clear business insights, no fluff. Add counterintuitive takes.” LLMs don’t just compress—they can refactor content to match mental models. #GrowthHacking #Marketing #AIwriting

This work was a part of my time at @IBMResearch as an intern. Huge thanks to my co-authors! Shubham Gandhi (@shubhamrgandhi), Jason Tsay (@jsntsay), Jatin Ganhotra (@JatinGanhotra ), Kiran Kate, Yara Rizk (@serialorganizer) #NeurIPS2025 #LLMAgents #RewardModels #AIForCode

@OpenAI GPT-5 Codex: your new coding teammate OpenAI just introduced GPT-5 Codex, a version of GPT-5 designed specifically for agentic coding. It’s no longer just an assistant—it acts as a real software collaborator, capable of handling quick tasks or working autonomously for hours on complex projects. Here are the key highlights 👇 🚀 What GPT-5 Codex can do 🔹 Trained on real-world software engineering tasks: from building projects from scratch to testing, refactoring, and code reviews. 🔹 Produces cleaner, higher-quality code with better adherence to “agent-style” instructions. 🔹 Adapts to context: instant responses for small tasks, sustained autonomous work (over 7 hours tested) for bigger challenges. 🛠️ Upgraded tools 🔹 Codex CLI: attach images (screenshots, wireframes…), use integrated web search, and manage permissions more easily. 🔹 IDE Extension: works with VS Code and forks, moving work seamlessly between local and cloud environments. 🔹 Cloud Codex: setup time reduced by up to 90% thanks to optimized container caching. 🔹 Frontend support with images: feed in design specs and get verified outputs with screenshots. ✅ Automated code review 🔹 Automatically reviews pull requests, checks for vulnerabilities, dependencies, and intent. 🔹 Can respond to direct commands like “@codex review for security vulnerabilities”. 🔒 Security & availability 🔹 Runs in a sandbox by default with network access disabled, only requesting permissions when needed. 🔹 Available across multiple ChatGPT plans (Plus, Pro, Business, Edu, Enterprise). 🔹 Soon available as an API model for CLI workflows. With GPT-5 Codex, AI is no longer just a copiloto—it’s becoming a full member of the dev team. 🔔 Follow me for the last AI updates! #OpenAI #GPT5Codex #AIforCode #SoftwareEngineering #AInews

2/6 👨‍💻 Coding super-powers: In the live demo GPT-5 whipped up a fully-functional French-learning web app—quizzes, flashcards and a mini-game—from a single prompt. Cleaner code, faster builds, built-in bug-hunt. #DevCommunity #AIforCode

3️⃣ GROK 4 CODE (beta) 💻 Built just for devs: •256K token context •Smart debugging •Refactoring •Code suggestions An AI pair programmer on steroids. Now in private beta testing. #DevTool #AIForCode

Most smart contracts are complex, unreadable, and unverified. @OpenledgerHQ is solving that. With AI-enhanced verification tools, devs can: 🔹 Catch bugs early 🔹 Understand risks fast 🔹 Build with confidence The next generation of devs will read smart contracts like books. @cookiedotfun #Web3Dev #AIForCode #SmartSecurity

Software ate the world. Now AI is eating software... #vibecoding #aiforcode #designtocode

GitHub Copilot remains a pioneer in AI-assisted coding. #GitHubCopilot #AIForCode

🚀 Build Real-Time #Codebase Indexing for LLMs with Tree-sitter for coding agents. ~100 lines of Python Real-time updates, syntax-aware chunking. Production-ready. Ultra-performant. Fully #OpenSource. get started: 🔗 cocoindex.io/blogs/index-cod… repo: 🌟 github.com/cocoindex-io/coco… Power your AI coding assistant with: ✅ Tree-sitter for syntax-aware code chunks ✅ SentenceTransformer embeddings ✅ Real-time updates with incremental processing ✅ Built for RAG, blazing fast Perfect for AI-powered #devtools and #semantic code search. #LLM #RAG #AIForCode #CodeSearch #DevTools #RealtimeAI #OpenSource #AIInfra #Rust #Python #TreeSitter #VectorDB #Embeddings #AIEngineering #GenerativeAI #Codex #CodingAgents #Claude #Cursor

Proud to share our new work — SWE-bench-Live is now live! A live-updating benchmark for real-world bug fixing, where even top agents like Claude 3.7 Sonnet OpenHands stumble. Try it out & follow us 👉 swe-bench-live.github.io #LLM #SWEbenchLive #AIforCode

Readings shared May 30, 2025. jaalonso.github.io/vestigium… #AIforCode #Agda #GenerativeAI #ITP #LeanProver #Math #Rocq