How to build a coding agent with GPT-5.1 and Context7

OpenAI just raised the limits for Codex and released GPT-5-Codex-Mini. The new model trails the full GPT-5-Codex by only 3 percentage points on the SWE-bench Verified benchmark but it’s 4× more cost-efficient. OpenAI recommends using it for lighter coding tasks to save requests to the main model. And once you hit 90% of your usage limit, Codex will politely suggest switching to the Mini version. On top of that, ChatGPT Plus, Business, and Edu users are getting 50% higher limits. Still not Anthropic-level generosity but it’s a solid upgrade.

Cursor has rolled out full integration of OpenAI’s brand-new GPT-5.1 Codex suite, pushing agentic coding into a new performance tier. Here’s what’s now active inside Cursor: GPT-5.1 for reasoning and high-level planning GPT-5.1 Codex for heavy multi-file engineering tasks GPT-5.1 Codex Mini for rapid-fire edits and quick fixes Why this matters for engineers and teams: Handles complex refactors with minimal oversight Accelerates prototyping by an estimated 3–5× Improves debugging reliability across large repos Generates tests and edge-case coverage automatically Works directly inside the IDE workflow devs already rely on The result: A significantly more autonomous, context-aware coding partner. Which model are you planning to test first — the full Codex powerhouse or the Mini for speed?👇

Get 96%+ of GPT-5-Codex programming performance at 1/5 the cost. OpenAI's new GPT-5-Codex-Mini offers developers a high-value coding option. 1️⃣ Performance: Scores 71.3% on SWE-bench, only a 3.2% gap from the full version (74.5%). 2️⃣ Cost: Significant savings (Input: $1.50/1M tokens). Complete similar tasks at ~1/5 the cost of the full version. 3️⃣ Smart: Suggests switching to Mini at 90% API quota usage to prevent interruptions. 4️⃣ Use Cases: Ideal for low-to-medium complexity tasks, code completion, CLI, and IDE extensions. Enable in Codex CLI: codex --model gpt-5-codex-mini Or set as default in config.toml. API access is "coming soon," but it's available now in the CLI and VS Code extension. Pricing: https://lnkd.in/gaSk3ADc Changelog: https://lnkd.in/gfuZEBkM #GPT5 #Codex #OpenAI #AICoding #Productivity

Forget about typing code. That era is gone. As of now, there are multiple open source LLMs for code generation. For example, StarCoder2, DeepSeek Coder, Llama. You can fine tune one of them on your code base. Paid LLM options like OpenAI, Gemini, Grok are also affordable. #AI #Coding

OpenAI just upgraded Codex with GPT-5-Codex — a new model that can think dynamically for up to seven hours on complex coding tasks. Here’s how it changes the game for developers and why it outperforms competitors like Claude Code, Cursor, and GitHub Copilot. #CursorAI #ClaudeCode #ChatGPT #OpenAI #MachineLearning https://lnkd.in/ec8byH8d

Went through a MarkTechPost and created the document. Source : https://lnkd.in/d4wGKSpK Code-oriented large language models moved from autocomplete to software engineering systems. In 2025, leading models must fix real GitHub issues, refactor multi-repo backends, write tests, and run as agents over long context windows. The main question for teams is not “can it code” but which model fits which constraints. Here are seven models (and systems around them) that cover most real coding workloads today: 1. OpenAI : GPT-5 / GPT-5-Codex 2. Anthropic : Claude 3.5 Sonnet / Claude 4.x Sonnet with Claude Code 3. Google : Gemini 2.5 Pro 4. Meta : Llama 3.1 405B Instruct 5. DeepSeek-V2.5-1210 (with DeepSeek-V3 as the successor) 6. Alibaba : Qwen2.5-Coder-32B-Instruct 7. Mistral : Codestral 25.01 The goal of this comparison is not to rank them on a single score. The goal is to show which system to pick for a given benchmark target, deployment model, governance requirement, and IDE or agent stack.

𝐘𝐨𝐮 𝐀𝐫𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐆𝐞𝐧𝐀𝐈 𝐀𝐥𝐥 𝐖𝐫𝐨𝐧𝐠. Stop trying to build GPT from scratch. 99% of ‘GenAI Engineer’ jobs are not about training base models. GenAI engineering isn’t about fine-tuning fancy LLMs. It isn’t about building base models from scratch. It’s about mastering the five connected layers that make GenAI systems actually work in production. 1️⃣ Master the APIs Stop reinventing the model. Start controlling it. → Learn how to call and handle OpenAI, Anthropic, and Google Gemini APIs. → Understand inputs, outputs, tokens, rate limits, and error handling. → Build wrappers that make LLM calls reliable and reusable. → Focus on latency, cost, and throughput tradeoffs. Because every GenAI system begins with one API call — and scales from there. 🎓 Resources: https://lnkd.in/gR8kj6cS  https://docs.anthropic.com  https://lnkd.in/g-iQtY9F  Watch: https://lnkd.in/gXb3VgBA 2️⃣ Prompt, Context & Function Calling A prompt isn’t a question. It’s a runtime instruction. → Learn prompt design, context management, and function calling. → Build prompts that control structure, style, and reasoning. → Use few-shot, chain-of-thought, and JSON schema outputs. 🎓 Resources: https://lnkd.in/gFi5zDj6  Watch:https://lnkd.in/gSPTEQRK 3️⃣ Frameworks Once you understand APIs and prompting — stop wiring prompts manually. Start building pipelines. → Learn LangChain to orchestrate chains, tools, retrieval, and memory. → Learn LangGraph to model multi-agent reasoning and workflow graphs. → Build modular, testable GenAI systems — not Jupyter hacks. 🎓 Resources: 🔗https://lnkd.in/grgNBFYW  🔗https://lnkd.in/gU56prcw 4️⃣ RAG Systems RAG (Retrieval-Augmented Generation) is how you make LLMs useful. → Inject external knowledge into your models. → Build retrieval pipelines with Pinecone, Chroma, or Weaviate. → Learn chunking, vectorization, and semantic search. → Evaluate retrieval quality and context window performance. RAG isn’t about databases — it’s about relevance and reasoning. 🎓 Resources: 🔗https://lnkd.in/gbEb8EdC  5️⃣ Agents Agents are the next layer of intelligence. → They plan, reason, and call tools autonomously. → They coordinate between memory, retrieval, and actions. → They form multi-agent workflows that mimic human collaboration. → Learn to design controlled autonomy, not chaos. 🎓 Resources: 🔗https://lnkd.in/gfqQXSbw 🔗https://lnkd.in/gyqp-TCw The hype is about the models. The work is in the systems. ♻️ Repost to help your network build real AI systems

In this tutorial, you will learn how to use OpenAI's Codex to ship your first change from a GitHub repository without writing code by hand — connecting a repo, planning changes, implementing them with AI agents, and opening pull requests. https://lnkd.in/dPtizW9F

Reproducing GPT-2 (125M) from Scratch! Ever wondered how large language models actually learn to write text? To explore that, I built a full reproduction of OpenAI’s GPT-2 (125M parameters) entirely inside a Jupyter Notebook, step by step, using PyTorch. This project goes beyond just training a model — it’s about understanding how it all works under the hood: 🔹 Data preprocessing and tokenization 🔹 Transformer architecture implementation 🔹 Training dynamics and loss optimization 🔹 Text generation and interactive exploration It’s a hands-on, educational project designed for anyone who wants to learn the mechanics of large language models in a clear, modular, and interactive way. 👉 Check it out on GitHub: https://lnkd.in/d4y7t8-W #AI #DeepLearning #PyTorch #GPT2 #LLM #MachineLearning #ArtificialIntelligence #OpenSource #Education

🚀 GitHub Copilot has officially integrated with OpenAI Codex, marking a major step forward in AI-assisted programming. This new integration supercharges Copilot with Codex’s advanced reasoning and contextual understanding, making it far more capable than traditional code completion tools. The upgraded Copilot can now handle complex coding tasks — from intelligent refactoring and test generation to detailed code reviews and feature scaffolding — all while adapting to your project’s unique context and coding style. Getting started is simple: Update to the latest version of the Copilot extension in VS Code (or your preferred IDE). In the model picker, select the new Codex or GPT-5-Codex option. Start using natural language prompts like “generate tests for this class” or “optimize this function for performance.” Teams on Pro, Business, or Enterprise plans can enable Codex access through admin settings, allowing organization-wide adoption. This integration represents more than an upgrade — it’s a shift in how developers collaborate with AI. With Codex, Copilot moves beyond suggestion-based coding to become a true intelligent assistant for the entire software lifecycle. #GitHubCopilot #OpenAICodex #AI #SoftwareDevelopment #DeveloperTools #FutureOfWork