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4,820 ideas

Laminar (YC S24) – RL-trained model that finds errors in agent traces

Hi HN, we’re Robert and Din from Laminar (https://laminar.sh). We’ve been building open-source observability for AI agents since 2024. Recently, we released flow-1, a model trained with RL to find errors in agent traces. On our benchmark (https://laminar.sh/blog/flow-1), it matches GPT-6-sol in detection quality while being 23x cheaper. This makes it affordable to analyze every production agent trace, without sampling.Here’s a one-minute walkthrough: https://youtu.be/KJa3h6ZJNLQWe also put together a post with example traces and analyses from flow-1 and other models: https://laminar.sh/blog/flow-1-in-actionAgent observability is crowded, so instead of a bullet list of how Laminar differs from X, we’ll explain how we’re solving agent observability from first principles.Agents now run for many steps and fail implicitly. An agent can click the wrong button or cite the wrong document while every call completes successfully. These errors are the most useful ones to catch and understand. To find them, you need to understand what the agent was trying to do and inspect its LLM calls and tool results. Simply looking at the final agent output is not enough.Collecting traces alone doesn’t solve this. A lot of agent observability platforms are still data collectors at their core. You can end up paying to store traces that nobody has time to read or extract value from.Using another LLM to inspect traces is THE solution, but capable models are expensive at scale and cheaper ones often miss errors or flag unrelated things.So we built our model to solve exactly this problem. We finetuned a small OSS model and then post trained it with RL on thousands of synthetic investigations of realistic agent workflows in software, legal, customer support, etc. We got a model that is both good at agent trace analysis and very cheap to run.flow-1 runs inside a harness we call Signals. It acts as a coding agent, treating an agent tr

Hacker News11d agoToolAI

82FL score
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How do you deal with AI output of your boss?

So, my boss goes on a rampage. You can clearly see the joy and at the same time the rage and impatience. AI is the tool he always wanted. We are too slow. Process is out of the window. No need for sprints anymore. No planning, just doing. All people downstream must somehow adapt.Whenever i see now his automated messages in slack, my natural reaction is: why do i even bother reading that? Thats for machines to read. He does not need me? Clearly i am not made for that. What is he expecting me to do? To confirm how great the AI did the job? Should i remember all these perfect presentations AI has created and integrate it into my doings into the coming weeks?I am torn. Should i really free my AI and deal with all this incoming text? Is this the way to react on this perfection?I am not mad at him, it clearly frees him of all the burder of dealing with people.I just dont know how to deal with this output personally. It is not for humans.I feel left alone. This is not a company anymore. Everyone is talking to AI now. This feels like chaos. Where everyone can do everything and stepping into each other's domain and it is perfect. The only way AI is stopped at are the ACLs.And because AI does everything for me, i don't remember things anymore, as i have no emotion connected with it. Feels like bad schooling and memoizing and get through it and you can't as it so dry and lifeless.So, how people deal with it in a company context? How have they setup their machines to react and still be up to date without reading the constant stream of changes coming from others?

Hacker News13d agoArticleAI

62FL score
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I built a task system to handle the drudgery so I could focus on tasks

I'm a software engineer from the east coast (who has lived in Boston, NY, the bay area, UK, France), and I built a task system called 3 & Done. It's the place you trust to store everything you need to do. It accepts voice input for rapid entry, as well as text (it stores the audio locally so you can listen to jog your memory) and emails you every morning with the three most important things for you to do that day, and why, based on what you've said about the task: deadlines, urgency, sitting too long.I've been at software engineering for about 44 years now... Grew up with it, which was unusual when I started, and I was always good about getting into the zone and doing things. The career started small, small company, then back office, the dot com startups, then smaller companies again after the bust, a non-profit, and eventually YouTube (Google at the time, but just barely) and Meta (just Facebook when I was there).When I hit the startups, and later big tech, it was no longer enough to just be an engineer, to have drive, to be good, you had to be organized, you had to follow through, get stuff done, and that's where the task systems started. Twenty-five over the years (and yes, I've actually counted), from Moleskine notebooks with Getting Things Done, evernote (remember evernote?), bug trackers, todoist, asana, google keep, among others.When I managed on the Facebook News Feed, I got into gantt charts (for me and my team), spreadsheets, OmniPlan... I remember apologizing to my director for being so disorganized (because I felt I was making a mess of it), and he told me that I was the most organized engineer he knew.That was because I was doing all of the work in the background, the trimming, the sorting, the reviews, but it was just so much work. Which was why I changed from system to system. Each time it built up, and I had to spend more time looking at things I couldn't or didn't get done, when I was rushed and could

Hacker News12d agoToolAI

75FL score
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KiviDB, a faster Redis-compatible memory database and vector cache

Hi all,So I have been working on this project KiviDB from February 2026. It started as a project for me to understand rust, having a product with no GC pauses, memory blow up after snapshots and getting OOM on running pods, and that was actually an issue I faced with Dragonfly. I wanted to have something picking the best of Rust, but also picking the best parts of other alternatives like Garnet and Dragonfly. I used AI a lot, but I was the one making the design decisions based on the tests that claude picked from my history of troubleshooting sessions as part of my job. KiviDB supports both RESP2 and RESP3 protocol. I had some founders credits on my AWS account which helped me with the infra, and running instances I could perform benchmark on.Its keyspace is sharded across threads, it is multi-threaded in nature, and performs best on Linux due to io_uring, I used the tokio_uring crate for this. It also has tokio fallback for other OS's. I hadn't open sourced it until recently as I felt it was not ready yet, the best way to learn it was to build a cloud managed service for it across all three widely used cloud providers i.e AWS, GCP and azure. Since I used to mainly work with cloud, I had some insights on what would be the best ways for the deployments, how to conduct failovers, manage SLAs, etc. And that's what I took help from claude to fast pace it. The website does look like AI slop but it works for now, given my limited resources, that's all I had. You may check it out at : https://kividb.io, I open sourced the core engine under AGPLv3, didn't want to have just source available, because that is available with other tools too. It supports apprx 98-99% of the redis 8.10 commands, few have issues and you can find them at https://github.com/kividbio/kividb/issues, contributors are welcome.Now the numbers, for the final benchmark, I used 2 c7gn.metal servers, in the same placement group in us-east1, and I us

Hacker News12d agoToolAI

78FL score
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