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Our open-source devtool is trending #1 on Product Hunt! Beacon is self-improving memory for coding agents. 🫶 I’d love your support: → Turn past debugging sessions into reusable skills → Share learnings across Claude Code, Cursor, Codex, and 20+ harnesses → Search and replay past sessions → Stop wasting...

529,126 次观看 • 3 天前 •via X (Twitter)

67 条评论

Justin D'Souza 的头像
Justin D'Souza3 天前

2 weeks since launching Beacon and our $5.5M seed announcement 🚀 → 3K+ GitHub stars → 300% Discord growth → 85%+ Cloud user growth → 6,200+ actionable findings The response has been incredible. Thank you all! Support us on Product Hunt ❤️

Paul Mit 的头像
Paul Mit3 天前

can't believe how much time and patience Beacon will save 😎

Justin D'Souza 的头像
Justin D'Souza3 天前

Glad to hear that Paul! Would love your feedback on the project :)

Peggy Rayzis 的头像
Peggy Rayzis3 天前

Upvoted on PH! I hope Beacon wins #1 🏆

Justin D'Souza 的头像
Justin D'Souza3 天前

thank you Peggy! 🙏 to the product hunt algo gods :)

Gina Acosta 的头像
Gina Acosta3 天前

this solves a problem developers encounter every single day 🙌

Justin D'Souza 的头像
Justin D'Souza3 天前

Thanks Gina! If you get a spare sec, please upvote our launch on product hunt and I'd love your feedback on Beacon :)

Catalin 的头像
Catalin3 天前

Really cool. I jump between T3 Code, Cursor and Codex, so this is great. Also open source. 🔥

Justin D'Souza 的头像
Justin D'Souza3 天前

Amazing Catalin! Would love your feedback on Beacon, let me know what you think :)

Catalin 的头像
Catalin3 天前

Sure thing!

Sharon Riley 的头像
Sharon Riley3 天前

Finally agents can learn from experience.

Justin D'Souza 的头像
Justin D'Souza3 天前

It's indeed finally here! Would really appreciate your support with an upvote on our launch :)

𝐒𝐈𝐆𝐍𝐀𝐋 的头像
𝐒𝐈𝐆𝐍𝐀𝐋3 天前

Let’s go 🦾

Justin D'Souza 的头像
Justin D'Souza3 天前

Thank you!!!

Asymptote Labs 的头像
Asymptote Labs3 天前

Let's go!!! 🚀🚀🚀

Katyayani Shukla 的头像
Katyayani Shukla3 天前

300% Discord growth in two weeks says people really needed this

Justin D'Souza 的头像
Justin D'Souza3 天前

I think so too :) Please join our Beacon community and keep the product feedback coming!

E1ev3n. 的头像
E1ev3n.3 天前

Turning past debugging sessions into reusable skills is the memory feature I actually miss. I keep my own notes in a searchable file, because an agent that starts from zero every session costs me the same lesson twice.

Justin D'Souza 的头像
Justin D'Souza3 天前

Oh wow, definitely check out Beacon -- I'd love to get your feedback on the product and hope it becomes a superpower for you :)

joyce 的头像
joyce3 天前

stars growing quickly!

Justin D'Souza 的头像
Justin D'Souza3 天前

Just getting started :) Thanks Joyce!

Shushant Lakhyani 的头像
Shushant Lakhyani3 天前

Sharing this with all my builder friends ASAP

Justin D'Souza 的头像
Justin D'Souza3 天前

Thank you Shushant! Grateful for your support :)

Karan 的头像
Karan3 天前

congrats on #1 open source makes this even better

Justin D'Souza 的头像
Justin D'Souza3 天前

We love OSS! I'd love your feedback on Beacon if you check it out Karan :) Don't forget to upvote our launch on Product Hunt:

toma 的头像
toma3 天前

LFG team

Justin D'Souza 的头像
Justin D'Souza3 天前

thanks Toma! really appreciate your support :)

Aaliya 的头像
Aaliya3 天前

n u got the support n upvote

Justin D'Souza 的头像
Justin D'Souza3 天前

Thanks Aaliya! Really grateful for your support 🙏

Nova Tech 的头像
Nova Tech3 天前

Huge congrats on number one Beacon sounds incredibly useful

Justin D'Souza 的头像
Justin D'Souza3 天前

Thank you Nova Tech! We're just getting started with Beacon, so much more to build :) Would really appreciate an upvote on our launch if you get a spare sec:

shirish 的头像
shirish3 天前

does the memory sharing work smoothly if i switch between cursor and claude code

Csaba Kissi 的头像
Csaba Kissi3 天前

Great work, Justin! This is exactly what coding agents need! 🙌 Curious how much time and tokens we could save if agents actually remembered what worked in previous sessions.

Justin D'Souza 的头像
Justin D'Souza3 天前

Exactly Csaba! We actually surface time + token savings in our agent session behavioural findings on our Beacon Cloud product :) Check it out here:

Rez Karim 的头像
Rez Karim3 天前

congrats on the launch. the cross-harness handoff alone is worth it for me, i bounce between claude code and codex way too much

Justin D'Souza 的头像
Justin D'Souza3 天前

Amazing, so glad to hear this Rez! I'd love to get your feedback on Beacon as you use it:

Rez Karim 的头像
Rez Karim3 天前

Sure, count me in :)

Nirmal B 的头像
Nirmal B3 天前

@shakshukan moving fast guys! love it 🚀

Justin D'Souza 的头像
Justin D'Souza3 天前

@shakshukan Yessir! Thanks Nirmal :)

Loki 的头像
Loki3 天前

sharing learnings across claude code and codex is the real hook. do old skills get pruned when the codebase changes, or does the memory just keep growing?

Justin D'Souza 的头像
Justin D'Souza3 天前

Great question Loki! Beacon uses traces from your agent sessions as a feedback loop to identify when a skill stops being useful or becomes stale as the codebase evolves. The goal is to update or retire outdated learnings, so agents get better over time instead of accumulating bad context :) Get started here:

Loki 的头像
Loki3 天前

coool

Justin D'Souza 的头像
Justin D'Souza3 天前

Thanks Loki!!

AI Panda 的头像
AI Panda3 天前

3K stars in two weeks is crazy. congrats Justin and team

Justin D'Souza 的头像
Justin D'Souza3 天前

Thank you!!! If you get a spare sec would really appreciate an upvote on our product hunt launch:

Vincenzo Nudo 的头像
Vincenzo Nudo3 天前

Memory built from debugging sessions goes stale fast. A fix learned on last month's code keeps getting applied after a refactor, so I'd tie each skill to the files it came from and expire it when those files change.

Justin D'Souza 的头像
Justin D'Souza3 天前

Totally agree Vincenzo! Tying skills to the files and codebase state they came from is a great signal. Beacon also want to use traces as a feedback loop. This means if a skill stops working after a refactor, Beacon will catch that and update or retire it automatically!

Rokas Ambrazevicius 的头像
Rokas Ambrazevicius3 天前

Turning old debugging sessions into skills is the part that actually saves tokens. How does Beacon decide a learned skill has gone stale after the code changes?

Christopher 的头像
Christopher3 天前

self-improving memory is a bold claim for a json file

kanav 的头像
kanav3 天前

super cool! upvoted :)

Justin D'Souza 的头像
Justin D'Souza3 天前

Really appreciate it Kanav! If you try Beacon out let me know, I'd love to get your feedback on the product :)

Vishal Singh 的头像
Vishal Singh3 天前

every bug fix and workaround carrying over across coding agents instead of starting from zero every time you switch tools. love startups solving the memory layer problem 🧠✨

Valdo 的头像
Valdo3 天前

LFG!🔥🤝

Justin D'Souza 的头像
Justin D'Souza3 天前

Thanks Vlado! We're just getting started 🚀🚀🚀

Valdo 的头像
Valdo3 天前

W!!

Justin D'Souza 的头像
Justin D'Souza3 天前

🫶

EDDY VU 的头像
EDDY VU3 天前

Congrats on the launch, the minimal footprint and response time look super clean!

Danylo Pravda 的头像
Danylo Pravda3 天前

Hoping it keeps the non-debugging stuff too. The tenth time you tell it which button style the app uses hurts more than any bug

Justin D'Souza 的头像
Justin D'Souza3 天前

100% Danylo! It's not just about debugging! Beacon will remember your coding conventions, UI preferences, architecture decisions, and all the little corrections you keep making. Give it a whirl here:

Veyra | AI & Tech 的头像
Veyra | AI & Tech3 天前

Self-improving memory is exactly what agents need

Justin D'Souza 的头像
Justin D'Souza3 天前

Love to hear it! Would really appreciate an upvote on our product hunt launch if you get a spare sec :)

David Liu 的头像
David Liu3 天前

love that its open source and local-first

Justin D'Souza 的头像
Justin D'Souza3 天前

Thanks David! Definitely try it out and let me know what you think (I'd be grateful for a ⭐️) :

Olivia Parker 的头像
Olivia Parker3 天前

handoff resume is useful. who signs the memory?

Justin D'Souza 的头像
Justin D'Souza3 天前

Thanks Olivia! Beacon has a configurable human-in-the-loop step so you can look at any memory candidates that are generated and sign off before they're automatically used by your agents!

Alex / AI Experiments 的头像
Alex / AI Experiments3 天前

This is really cool! Does Beacon also work across different projects? Or is the memory tied to a specific repo?

离岸工具包🎒 的头像
离岸工具包🎒3 天前

Looking at that trending product! 🌟 What a feat with Beacon. Impressive! 🚀

相关视频

I gave Claude Code, Codex, and Cursor a self-improving, shared MEMORY. Now my coding agents learn from each other, and their knowledge follows me wherever I work. Coding agents work in silos. Your corrections in Cursor don't carry over to Claude Code, and skills built in Codex don't automatically transfer to other harnesses. So switching tools often means starting from scratch. What if every coding agent could learn from everything you've already done? I invited Justin D'Souza, co-founder of Asymptote Labs, to show me how to make this possible with Beacon, a 100% open-source memory layer for coding agents. We set it up live, explored real coding sessions, and watched Beacon turn past work into reusable knowledge. Here's what I learned: 1. One memory layer for 20+ coding agents. Beacon captures activity across Claude Code, Cursor, Codex, and 20+ harnesses using OpenTelemetry, hooks, and local session files. Everything gets normalized into a single format, regardless of which agent you're using. 2. Your entire coding history becomes searchable. Every prompt, tool call, command, file change, and response is captured in a structured trace. Instead of losing valuable context when a session ends, you build a searchable history of everything your agents have done. 3. Switch agents without starting over. Hit your usage limit in Cursor? Open Claude Code and ask it to continue your latest Cursor session. Beacon retrieves the relevant context in milliseconds so your next agent can pick up where the last one left off. 4. Your agents learn from your past work. This is where things get interesting. Beacon identifies repeated workflows and turns them into reusable skills. In our demo, it discovered a recurring UI development workflow and generated a skill containing the exact dev server commands, screenshot steps, and deployment instructions. You teach one agent something once. Every other agent can benefit. 5. Stop accumulating skills nobody uses. Have 100 skills but only use five? Beacon analyzes actual usage across thousands of sessions and identifies skills worth keeping, combining, or removing. Your agent's memory gets more useful, not just bigger. 6. Security rules that follow you across every harness. Beacon includes hundreds of detection rules that work across agents. It can identify risky behavior, including sequences where an agent reads sensitive credentials and then makes an outbound network request. 7. It even caught a real credential leak. While reviewing Justin's actual Cursor history, Beacon surfaced an incident where part of a production database password had been sent to a model provider. That's the power of having visibility into what your agents are actually doing. 8. One prompt to get started. Completely open source. Paste a setup prompt into your coding agent and you're off. Keep everything local on your machine, or use Beacon Cloud for free. If you're juggling multiple coding agents, you need to check this out. Drop a like and share it with someone facing the same issue. Beacon on GitHub: (don't forget to star 🌟) Enjoy the episode. Cheers!

Akshay 🚀

54,039 次观看 • 16 天前

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Akshay 🚀

121,058 次观看 • 1 个月前

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

293,860 次观看 • 1 个月前