Codex Enhancements for Mac and Tools Integration

Anthropic shipped 74 product releases in 52 days. One every 1.4 days. 60-100 internal deploys per day. The result? Claude Code hit $2.5B in annualized revenue. Market dominance, fast. But the quality debt is coming due. Opus 4.6 broke 50 concurrent agent sessions at AMD when a silent update cut thinking depth by 67%. Opus 4.7 was labeled "legendarily bad" on Reddit within 24 hours of shipping. Quality complaints escalated 3.5x month-over-month. 132 service incidents in 90 days. Meanwhile, OpenAI's Codex took the opposite path. Started rough — 40-60% success rates. But instead of shipping features, they fixed the failures. By March 2026: 85-90% reliability. The Stack Overflow data is the real story: developer trust in AI tools dropped from 40% to 29% between 2023 and 2025 — while adoption rose to 84%. More people using tools they trust less. One developer with 1,060 upvotes put it simply: "We don't want magic. We want a predictable tool." Speed wins market share. Quality wins trust. Which one compounds? Read the full analysis: https://lnkd.in/gZJfqmhF #AITools #ClaudeCode #Codex #DeveloperExperience #SoftwareQuality

65% of developers prefer Codex. But Claude Code wins 67% of blind code quality tests. Something does not add up. The Reddit debate "Is Codex really getting better than Claude Code?" keeps resurfacing. After digging through benchmarks, 500+ developer opinions, and the actual architecture of both tools, the answer is more nuanced than either side wants to admit. Here is what the data actually shows: - SWE-bench Verified: essentially tied (80.9% vs 80%) - Terminal-Bench: Codex leads by 12 points (77.3% vs 65.4%) - Token efficiency: Codex uses roughly 4x fewer tokens for equivalent tasks - Code quality: Claude Code wins 67% of blind reviews The real story is not about which tool is "better." It is about a trust crater. Between March and April 2026, Claude Code shipped product-layer changes that degraded the developer experience — and Codex was there to pick up the slack. The developers shipping the most code right now are not picking sides. They are using both tools for what each does best. Which AI coding tool is your daily driver — and have you tried using both together? Read the full breakdown: https://lnkd.in/gP4bWCG2 #AIcoding #ClaudeCode #CodexCLI #DeveloperProductivity #SoftwareEngineering

OpenAI shipped 3 versions in 9 days inside Codex. The signal: it's becoming agentic infrastructure, not just a smart terminal. I went through `0.128.0` → `0.130.0` and pulled out the 7 changes that actually shift how Codex CLI feels in real use. OpenAI added persisted `/goal` workflows (start → pause → resume across sessions), `codex remote-control` for headless scriptable Codex, and Vim mode for power users who live in the TUI full-time. These aren't cosmetic updates. Taken together, they describe a product moving away from one-shot prompting toward something you run and orchestrate as a system. Are you tracking both Codex and Claude Code changelogs? The two products are shipping fast and diverging in interesting ways. Links to posts in the comments 👇

OpenAI launched a new "switch to Codex" feature that allows developers to migrate their entire coding environment to the Codex application with a few clicks. The tool enables the direct import of settings, plugins, custom agents, and specific project configurations to minimize setup downtime.

🚀 Bringing Sanity to LLMs: I built a local-first RAG Assistant in Go! Retrieval-Augmented Generation (RAG) is arguably the most elegant concept in the current AI era. Why? Because it bridges the gap between a model's "general knowledge" and your private, specific data—without the need for expensive fine-tuning. I decided to get under the hood and implement my own RAG engine from scratch using Go. Meet GoRAG. Why the Go stack? Internalizing the RAG workflow (Chunking -> Embedding -> Vector Search -> Context Injection) is one thing, but making it performant is another. Go’s concurrency model makes streaming responses feel like butter, and its type safety keeps the vector math predictable. The Tech Specs: - Storage: Swapped out simple JSON for BadgerDB. It’s a high-performance, embedded key-value store (LSM-tree based) that lives right in the binary. No external DB servers required, just pure Go-native persistence. - LLM Engine: 100% local via Ollama. Your data never leaves your machine. - Idempotency: Used SHA256 hashing for document chunks to ensure the vector store stays clean and duplicate-free. - Real-time: Implemented a streaming API using Fiber and a modern Web UI for that "alive" chat experience. Coolest Feature: The assistant doesn't just "know" things—it cites them. Every answer comes with source citations (e.g., [Source 1]), making the "hallucination hunting" much easier. The Geeky Details:  ✅ Multi-format support (.pdf & .md)  ✅ Smart chunking with configurable overlap  ✅ Dockerized for one-command deployment  ✅ Graceful shutdown for data integrity RAG isn't just a buzzword; it's the architecture of the future for private, reliable AI. Check out the code, star the repo, or try it yourself here:  👉 https://lnkd.in/dSEXz_VN #Golang #AI #RAG #OpenSource #BadgerDB #Ollama #MachineLearning #SoftwareEngineering #LocalAI

🚨 Anthropic and OpenAI shipped the same feature within weeks of each other — neither knowing the other was building it. Both Claude Code and Codex now have a /goal command. You define a completion condition once, and the agent keeps working across turns until it meets it. No prompting to continue. No babysitting. The formula is the same across both tools: /goal [task] · until [end state] · without [constraints] The convergence matters more than the feature itself. When two competing labs independently arrive at the same interaction primitive in the same month, that's the pattern becoming infrastructure — not a product differentiator. The cheatsheet below has the formula, a before/after breakdown, 6 ready-to-use prompts, and the pro tips worth knowing before you burn through tokens on a poorly scoped condition. Full comparison of how Claude Code and Codex implement it differently — link in the comments. What's the first task you'd hand off to /goal? #AgenticAI #ClaudeCode #LLM #AIEngineering #DeveloperTools

Shipping AI-generated code fast is great until you realize you don't actually understand what you merged. Learning Opportunities is an open-source Claude Code + Codex plugin that injects science-backed exercises after big refactors to rebuild the understanding you skipped. Worth a look. #DevTools Free and open-source (CC-BY-4.0). Works with Claude Code and OpenAI Codex. Git post-commit hook and repo orientation skill also included. Full details on the listing. https://lnkd.in/g-YjTwxB

we ship a small library of free dev tools at tools.voiddo.com. sixteen of them so far. jsonyo, tokcount, httpwut, logparse, cronwtf, regexlab, slugmint, fakeit, dotdig, portcheck, randumb, pricepulse, tzdiff, and a few more. zero ads. zero signup. each one solves a single specific problem in under thirty seconds. open the tab, paste your input, copy the output, close the tab. why free? because the marginal cost of running a static page that runs your regex in the browser is zero, and because the way most people find a tool company is by using one of their tools first.

GPT5.5 back on top! You can now verify DietCode's changes with a single-source of truth, DietCode 5.10.24 introduces WIKI! It gives you deep insight for technical awareness so agents and developers never get lost after several passes/turns!

Claude code just shipped /goal - a feature that has seen a lot of resonance since codex implemented it a few weeks ago. Same name, similar effect - but big differences in how it actually works. 👇 They look identical from the outside: "keep working until X" but under the hood, they're built on completely different bets. One uses a SQLite-backed persistence layer and lets the model declare its own completion. The other is a thin Stop-hook that calls a second model (Haiku) to grade every turn against your condition based no what's visible in the transcript. Same surface but very different reliability stories. Our team prefers codex' implementation for long running tasks even though claude is the daily driver. Swipe through: implementation, pitfalls, and how to actually use either one effectively.