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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...

450,794 görüntüleme • 16 saat önce •via X (Twitter)

67 Yorum

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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 profil fotoğrafı
Paul Mit15 saat önce

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

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

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

Peggy Rayzis profil fotoğrafı
Peggy Rayzis16 saat önce

Upvoted on PH! I hope Beacon wins #1 🏆

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

Gina Acosta profil fotoğrafı
Gina Acosta16 saat önce

this solves a problem developers encounter every single day 🙌

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

Catalin profil fotoğrafı
Catalin16 saat önce

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

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

Catalin profil fotoğrafı
Catalin16 saat önce

Sure thing!

Sharon Riley profil fotoğrafı
Sharon Riley16 saat önce

Finally agents can learn from experience.

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

𝐒𝐈𝐆𝐍𝐀𝐋 profil fotoğrafı
𝐒𝐈𝐆𝐍𝐀𝐋16 saat önce

Let’s go 🦾

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

Thank you!!!

Asymptote Labs profil fotoğrafı
Asymptote Labs16 saat önce

Let's go!!! 🚀🚀🚀

Katyayani Shukla profil fotoğrafı
Katyayani Shukla16 saat önce

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

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

E1ev3n. profil fotoğrafı
E1ev3n.16 saat önce

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 profil fotoğrafı
Justin D'Souza16 saat önce

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 profil fotoğrafı
joyce16 saat önce

stars growing quickly!

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

Just getting started :) Thanks Joyce!

Shushant Lakhyani profil fotoğrafı
Shushant Lakhyani16 saat önce

Sharing this with all my builder friends ASAP

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

Thank you Shushant! Grateful for your support :)

Karan profil fotoğrafı
Karan16 saat önce

congrats on #1 open source makes this even better

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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 profil fotoğrafı
toma16 saat önce

LFG team

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

thanks Toma! really appreciate your support :)

Aaliya profil fotoğrafı
Aaliya16 saat önce

n u got the support n upvote

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

Thanks Aaliya! Really grateful for your support 🙏

Nova Tech profil fotoğrafı
Nova Tech16 saat önce

Huge congrats on number one Beacon sounds incredibly useful

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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 profil fotoğrafı
shirish15 saat önce

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

Csaba Kissi profil fotoğrafı
Csaba Kissi16 saat önce

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 profil fotoğrafı
Justin D'Souza16 saat önce

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 profil fotoğrafı
Rez Karim16 saat önce

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 profil fotoğrafı
Justin D'Souza16 saat önce

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

Rez Karim profil fotoğrafı
Rez Karim16 saat önce

Sure, count me in :)

Nirmal B profil fotoğrafı
Nirmal B15 saat önce

@shakshukan moving fast guys! love it 🚀

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

@shakshukan Yessir! Thanks Nirmal :)

Loki profil fotoğrafı
Loki16 saat önce

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 profil fotoğrafı
Justin D'Souza16 saat önce

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 profil fotoğrafı
Loki15 saat önce

coool

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

Thanks Loki!!

AI Panda profil fotoğrafı
AI Panda16 saat önce

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

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

Vincenzo Nudo profil fotoğrafı
Vincenzo Nudo16 saat önce

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 profil fotoğrafı
Justin D'Souza15 saat önce

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 profil fotoğrafı
Rokas Ambrazevicius15 saat önce

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 profil fotoğrafı
Christopher16 saat önce

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

kanav profil fotoğrafı
kanav16 saat önce

super cool! upvoted :)

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

Vishal Singh profil fotoğrafı
Vishal Singh16 saat önce

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 profil fotoğrafı
Valdo15 saat önce

LFG!🔥🤝

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

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

Valdo profil fotoğrafı
Valdo15 saat önce

W!!

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

🫶

EDDY VU profil fotoğrafı
EDDY VU16 saat önce

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

Danylo Pravda profil fotoğrafı
Danylo Pravda15 saat önce

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 profil fotoğrafı
Justin D'Souza15 saat önce

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 profil fotoğrafı
Veyra | AI & Tech16 saat önce

Self-improving memory is exactly what agents need

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

David Liu profil fotoğrafı
David Liu16 saat önce

love that its open source and local-first

Justin D'Souza profil fotoğrafı
Justin D'Souza16 saat önce

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

Olivia Parker profil fotoğrafı
Olivia Parker16 saat önce

handoff resume is useful. who signs the memory?

Justin D'Souza profil fotoğrafı
Justin D'Souza15 saat önce

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 profil fotoğrafı
Alex / AI Experiments16 saat önce

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🎒 profil fotoğrafı
离岸工具包🎒15 saat önce

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

Benzer Videolar

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 🚀

53,878 görüntüleme • 13 gün önce

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 🚀

120,930 görüntüleme • 1 ay önce

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

293,488 görüntüleme • 1 ay önce