SpecStory, Inc.

Do reachout if you are thinking along the same lines!

One weird thing about AI coding that doesn’t get talked about enough: when something breaks, the hardest part is no longer understanding what the code does. It’s understanding why it exists at all. Earlier, when the code was messy in codebase, we could usually track down the person who wrote it and get the story. “This was a shortcut.” “We were under a deadline.” “We tried three other approaches first.” There was always some context behind the mess. Now the code often comes from coding agents. The human merged it, but didn’t really author it. The prompts that generated those code are not saved normally and not shared with the team. Without the reasoning and intent, just the code is not very useful. So debugging and maintaining that codebase later becomes a lot harder since the decisions are lost. A better approach is to treat intent as part of the system, not as something disposable. Prompts, discussions, rejected alternatives, assumptions should live next to the code, not in someone’s chat history. Whether it’s versioning prompts, attaching reasoning to PRs, or storing agent conversations as first-class artifacts, teams need a memory layer for AI workflows. Code should be reviewable not just at the syntax level, but at the decision level. If AI is becoming a co-author, then its thinking should be part of the codebase too.

We’ve seen so many devs lose months of valuable work when chat histories disappear - all the reasoning, decisions, and progress gone in an instant. It’s awesome to see the Cursor team reminding users to use SpecStory for automatic chat backups and saving conversation histories. Keeping your AI chat history safe means keeping the why behind your work intact.

People who are using AI coding tools to build and implement production systems should seriously consider using SpecStory, Inc. While collaborating with the coding tools like Claude Code, Codex, Cursor, Copilot, etc., the conversations itself holds immense value. Those exchanges contain the reasoning behind technical decisions, implementation trade-offs, and architectural thinking. Losing them means losing a large part of your project’s context and decision-making history. SpecStory automatically saves these AI coding conversations and turns them into a searchable, version-controlled record. I have heard so many stories from our users where saving those conversations have been super helpful to them, basically like a memory layer for your coding tools. Do give it a try, and always there to help incase you people face any issue ;)

AI helps developers learn more and code more productively, but it creates a new bottleneck: it writes the what but loses the why. This makes maintenance and collaboration difficult. Jake and Greg with SpecStory, Inc. shared a 3-step workflow to keep the "why" and stay in control: Plan: Don't start with a vague prompt. Use simple Markdown files (spec md, tasks md) to set clear guardrails and define the work before you code. Implement: Guide the AI as a pair programmer. Don't just accept the output; intervene and make ad-hoc corrections as it works. Reflect: This is the key. After the session, sync the entire chat history. Use the AI to analyze that log and extract a clean decisions md file. This decisions md file becomes the summary for your pull request. It shifts the code review from focusing only on what changed to including why it changed, giving you a durable record of intent. Check out the full write up and video of the session https://lnkd.in/ew4-6ahQ

AI helps developers learn more and code more productively, but it creates a new bottleneck: it writes the what but loses the why. This makes maintenance and collaboration difficult. Jake and Greg with SpecStory, Inc. shared a 3-step workflow to keep the "why" and stay in control: Plan: Don't start with a vague prompt. Use simple Markdown files (spec md, tasks md) to set clear guardrails and define the work before you code. Implement: Guide the AI as a pair programmer. Don't just accept the output; intervene and make ad-hoc corrections as it works. Reflect: This is the key. After the session, sync the entire chat history. Use the AI to analyze that log and extract a clean decisions md file. This decisions md file becomes the summary for your pull request. It shifts the code review from focusing only on what changed to including why it changed, giving you a durable record of intent. Check out the full write up and video of the session https://lnkd.in/ew4-6ahQ

We had a chat with Mitchell Hashimoto, the creator of Ghostty, about what it’s really like maintaining an open source project in the age of AI He’s reviewed hundreds of AI-generated pull requests, and his takeaway is simple but powerful “Open source doesn’t have an AI problem. It has a transparency problem.” In our conversation, Mitchell shared: - How Ghostty now requires contributors to disclose when they’ve used AI - The rise of “AI code slop” and what separates good-faith contributors from noise - He’s also thinking beyond Git, about what version control might look like when code is co-written by humans and AI It’s part of a quiet shift in open source: AI isn’t replacing contributors, it’s testing how transparent we’re willing to be about our process. Full story below 👇

As AI coding tools are becoming a natural part of every developer’s workflow, open source collaboration is evolving too We teamed up with the amazing folks at DigitalOcean to publish a new guide: "How to Be an Open Source Hero: Contributing AI-Generated Code with Care" In it, we explore: - How to use AI coding tools responsibly in open source projects - Best practices for contributors and maintainers - When and how to disclose AI-assisted code - Real-world examples of ethical, transparent collaboration Aim is to make open source development more inclusive, transparent, and future-ready together.

🚀 SpecStory now supports Codex CLI One of the most requested features by our users is finally here: You can now auto-save every OpenAI Codex terminal session to structured Markdown in .specstory/history. What’s new: - Official support for Codex CLI in SpecStory CLI v0.11.0+ - `specstory run codex` launches Codex with automatic session capture - Sessions can be saved in clean, git-friendly Markdown (prompts, tool calls, code, timestamps) - Optional cloud sync if you want global search & sharing across projects How it adds value: - 🔁 Reproducibility: Re-run agent work with full context (not just diffs) - 📝 Reviews: Share the why behind code for faster reviews and onboarding - 🛡️Resilience: Treat agent output as a durable record you can search, reuse, and learn from Your Codex sessions now won't vanish, instead will stay as a lasting context. Docs: https://lnkd.in/gkTD4Ycu

AI has made it possible to build faster than ever. We type a prompt, generate the code, iterate until it works, and move on. But speed has a cost: the reasoning behind the code disappears. Weeks or months later, the context is gone, nobody remembers: - Why a decision was made - Why was it built this way - What alternatives were tried - How false starts shaped the design That context is as important as the code itself. SpecStory captures those details automatically. It preserves the conversation history that built the code: the “why” that usually vanishes once the tab is closed. That history isn’t just a transcript, it’s structured memory that users can reuse. One of our user, @isaac_flath, discovered this firsthand. His SpecStory logs didn’t just capture the final output, they preserved the messy process: the failed examples, the back-and-forth reasoning, the design debates. That history became the outline for his blog post. He didn’t have to reconstruct his thinking from scratch, it was already there in the conversation. Read the full story below 👇 Full Story: https://lnkd.in/eCxi7qhQ