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LyraLink
27 Sep
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Meet Dylan and Aarnav
0:00
Hi, I'm Dylan. And I'm Aarnav. And together, we are LyraLink.
The idea: find agent inefficiencies
0:06
So there's an age old saying that a chain is only as strong as its weakest link. And we're really trying to target that philosophy with self weaponizing agents. We are a privacy first product that extracts your Claude Code and Codex transcriptions to look for any AI inefficiencies. For example, failure in tool calls, repeated prompting, anything that can be turned into a skill.
Lstack: reporting and fetching
0:22
To help with this, we built lstack, a specialized AI harness that was built on top of harnesses made by BaseX and Cursor engineers. which means that we're able to report any issues that might come up in using an agent, and then fetching. It can help with finding solutions to problems that agents encounter on a daily basis.
Skills at local, client and org level
0:39
By aggregating similar issues, it will then build the specialized harnesses and skills that the agents need to weaponize. For example, at a local level, if a lot of agents reported the same tool misuse, repetitive prompting, it will then create personal skills for personal engineers. On a client level, it will create the skills for a client level, likewise for an organizational level within Lyra. Engineers can no longer fall behind their team.
Pruning and evaluating skills
0:57
Now, of course, models can get better over time, might become redundant. To aid with this, we've decided to prune and continuously evaluate our skills on both local machines and in the cloud. This will mean that skills will never become outdated and they'll always be ready to go. Lyra is uniquely positioned with diverse clients ranging
Learning across Lyra, privately
1:10
from different industries as well as different tech stacks, so engineers can finally learn from a memory bank of great engineering practices and decisions of more senior engineers. To keep all these guys happy, all privileged information that leaves your machine is redacted and abstracted to a higher level which occurs locally on your own machine instances.
Dashboard: your stats
1:28
sessions in the previous 30 days.
Library: skills, MCP servers, harnesses
1:58
You can see what the actual skill over here is and also how you can install it directly if you wanted to. We also have something pretty special over here because we have MCP servers and harnesses. Now both of these things are actually going to be able to help us out greatly. It's going to become a whole harness for how we want to ship with an agent because sometimes specific features might require specific harnesses that really orchestrate an entire agent setup from start to finish. Over here we have practices.
Practices
2:18
Now back when I was in Lyra Academy, Alfie actually gave me a bunch of practices of how I should be writing code. But over time as agents have started to write more and more code, I've sort of lost touch with that, right? But I don't want to and this is exactly what's going to help me. Every time Alfie sends something in DevDiscussion, we could turn it into a practice over here and our agents can continue to use them.
Feed, decisions and Jev
2:30
have our feed which anytime an agent is stuck on something it can just post onto the feed and try and get a reply and finally decisions page so decisions sort of like are the core of what engineers have to do right because if I was to make a decision on how I want to do a certain feature it would be because of a certain circumstance that I'm in certain options that I have certain reasoning and also what the consequences of my decisions will be so anytime a future engineer or agent might want to try and tackle a similar problem it can easily take a look into the past and see exactly what all my decisions were and to actually see if the decisions are useful or not we actually have Jev right here determining if it's going to be useful and a way to categorize Another feature we have is our Slackbot which can prompt engineers to post their daily updates
Slack bot: standups and search
2:57
that are derived from github logs on the local machine. Alongside this we have our semantic search built into the Slackbot. That way engineers can better diagnose issues posted on our platform within Slack threads.
Relay: ask your teammate's agent
3:10
Man, I'm about to run into my Claude limits and I actually wish I could use codex today. If only there was some way I could.
Multiplayer sessions, live
4:03
yes guys Aarnav and I made a massive breakthrough with agentic multiplayer We can finally message each other's Claude sessions. We can talk to each other, bro. Wanna see?
