Hacker News

So I built this book reader app to allow her to click on the words, sound out the phonemes, and read by herself.

My 12 year old son wrote the first story, there's a cliffhanger at the end and he's working on the next section.

We're going to release more chapters and more stories in the near future.

And there's one other thing -

The reader has an MCP connection, so you can connect your Claude or ChatGPT or Grok, give them a PDF of a book you like (or even a set of scans done quickly with a phone camera) and have them import your favorite books into the app (private to your account, for your own kid, if you own the book of course).

The reader is free to use on its website and open source if you want to tinker and create your own version.

The included stories are under Creative Commons.

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/KJa3h6ZJNLQ

We also put together a post with example traces and analyses from flow-1 and other models: https://laminar.sh/blog/flow-1-in-action

Agent 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 trace like a repo, with each span (an LLM call, tool call, etc.) as a file. It starts from a compressed string representation of a full agent trace and a user prompt, then greps across spans and reads the relevant ones in full.

The beauty of Signals is that it can respond to a general prompt. You can ask it to identify user frustration, check whether your browser agent completed a checkout, and so on. It’s not limited to raw error detection. It returns findings as JSON in a schema users can freely define. We call these signal events.

Once you’re analyzing every trace, you also have too many individual events to read. That’s why we automatically group similar signal events into clusters. You can see recurring problems and how often they happen, then drill into individual events and the traces behind them.

You can use traces from a cluster to build an eval dataset for that type of failure. You can then use your favourite coding agent to hillclimb on those evals and check that the fix works across these cases before shipping it to prod.

Trace → analyze → catch errors and form clusters → fix → release. Making this loop easy is why we built Laminar.

We’ve put the same thought into the rest of Laminar. Our blog (https://laminar.sh/blog) explains how looking closely at how agents actually work led us to build 20x trace compression, a custom SQL query engine and automatic task extraction.

Laminar is Apache 2.0 and self-hostable (https://github.com/lmnr-ai/lmnr). We make money from the managed cloud.

We’d love to hear how you track your agents’ performance today. How much do you learn from user feedback versus your own monitoring? And when you fix something, how do you check that it hasn’t introduced a different failure?

It sits between your code and the hardware. No humans in the loop! Maxwell makes the code run faster as it runs, without ever changing a single line.

Maxwell sees programs from the inside: traces, profiling information, and the data flowing through them. It adapts dynamically to the environment, including the hardware, what else is running, and what the inputs look like.

Code execution models change the way we approach coding. Today, Claude/Codex (and humans) keep re-optimising code for every different use-case hardware and data flow (think of LLM inference engines, like vLLM!). Our bet is that this is unnecessary, suboptimal, and that it does not scale. Instead, we think the right answer is to make code a living, thinking, thing.

Early access is open at heymaxwell.ai. Happy to answer questions here.

https://www.reuters.com/legal/government/how-one-hacker-used...

https://www.theverge.com/ai-artificial-intelligence/1013643/...

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 couldn't put time into today's review, it would pile up, and then using it was so much work that I would start taking mini-notes, just emailing myself a few things to do the next day.

That's actually the thesis of the app (but not where it started), that if I could just have a few things to do emailed to me each day, I could get it done.

This app actually started when I described my previous startup failures into a Claude web session, and it repeated back to me what I had just said. My previous failures were because I spent too much time building the stuff I needed, and not building what I was supposed to be building.

I decided that the app would email me three things to do every day (which matches best practices for attention), but that the day was always yours to decide. We won't overload you, but you can add more, if you're feeling up to it, or cut back, and we never shame and try our best not to nag (I often only finish two). If we get it wrong, you can tell us, and we'll re-rank it once a day, or you can set it manually, and if you do, we never override that choice. The web app is live, and native for both platforms is coming. In the meantime, on Android, you can use gestures to quick launch into the voice input so saving a task is only a gesture away (e.g. double-tap the back of a Pixel, double-press the power button on Motorola).

Now let's talk about the dirty parts. I used AI to help me build this. Anthropic models mostly, and the brains of the app come from Anthropic models (I like the quality, I like the privacy, absolutely no training on customer data, and the stance of the company). This app is not some one-shot wonder where I asked frontier models to build a task system and then I put it on a server to show you.

I have prompts for the various features (e.g. ranking, generating suggestions, task extractions), I have eval sets of task lists (not anyone's real tasks, but constructed from patterns in my tasks, or the tasks of testers who allowed me to fix the problems they were having), with the three I expect to come out. I store the eval outputs, and re-test whenever I add a new rule to a prompt, or whenever testing a new model version. The great thing about having this eval set is that it catches when I get it wrong. When I introspect a failed run, sometimes it's because I need to tune a prompt, but sometimes it's because I missed something in my own ranking.

I did the same thing with my infrastructure as well, so this time, instead of setting up servers, and building login systems, and managing databases, I built on Vercel (git push to serving Next.js), Neon (hosted postgres).

I started getting emails mailed to me by mid-June, but started getting usable suggestions on June 26, and I've been using it every morning since. I wanted it to be easy to get started, and easy to use, so the input handles mess (you can add multiple tasks in the same run-on sentence, and a friend who helped me test it just pasted his entire obsidian todo list in wholesale to get started).

I wanted to make sure that anyone could try it out, so I built a trial feature that uses the real in-app ranking with a list of items you provide.

If you want to try the service, there's a free 14-day trial (card required), and then it's $99/year or $9.99/month after that (plus sales tax). We warn you seven days before your card is charged and if you cancel, you keep access through the term (either the trial, or our last paid period). Online, through settings -> billing, no email, no call.

I really hope you'll try it out. If you do, please tell me if we did a good job picking out your day.

https://3anddoneapp.com/try

https://www.linkedin.com/posts/ali-hamie_im-an-engineer-at-m...

> I'm an engineer at Meta and I never felt this uncomfortable doing my job. I'm writing code these days and it all feels like a black box. This new standard of shipping with AI is moving so fast that if we're going to spend our time checking each line of code, it's going to take forever. That means you can end up with systems you've built where you don't truly understand how all the code works. You understand the high level, but not every detail. And maybe this is becoming the new standard. It goes against every instinct of being a software engineer. I'm used to understanding what I'm building. Now I'm trying to figure out how much I need to understand before I'm comfortable moving forward. Are other engineers feeling this too?

How are you handling this?

https://github.com/LostCityRS/Server