Mockapapella · HackerTrans

Mockapapella

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投稿

America Is More

thelisowe.substack.com

2 ポイント投稿者 Mockapapella19 日前0 コメント

FunnyBench – Can AI Models Tell Funny Jokes?

funnybench.lol

4 ポイント投稿者 Mockapapella先月2 コメント

Simple HTTP Server Implemented in Mov

github.com

2 ポイント投稿者 Mockapapella2 年前1 コメント

Show HN: Batteries Included AI Deployment

github.com

2 ポイント投稿者 Mockapapella2 年前2 コメント

Victoria Woodhull

en.wikipedia.org

2 ポイント投稿者 Mockapapella3 年前0 コメント

Web Design in 4 Minutes

jgthms.com

2 ポイント投稿者 Mockapapella3 年前0 コメント

Ask HN: Has anybody explored diffusion models as a basis for LLMs?

1 ポイント投稿者 Mockapapella3 年前3 コメント

How to run without all the pesky agonizing pain

dynomight.net

2 ポイント投稿者 Mockapapella3 年前0 コメント

Cognitive Complexity [pdf]

sonarsource.com

2 ポイント投稿者 Mockapapella4 年前0 コメント

Ask HN: Electric RV Options?

8 ポイント投稿者 Mockapapella4 年前9 コメント

17776

sbnation.com

24 ポイント投稿者 Mockapapella4 年前8 コメント

Twitter user brings imaginary childhood microwave friend back to life using GPT3

twitter.com

3 ポイント投稿者 Mockapapella4 年前0 コメント

Ask HN: Why does the Segway look so uncool?

7 ポイント投稿者 Mockapapella4 年前6 コメント

Sleeper Cell – a method of embedding invisible programs into source code

thelisowe.com

3 ポイント投稿者 Mockapapella5 年前1 コメント

Ask HN: What are some good Raspberry Pi alternatives?

48 ポイント投稿者 Mockapapella5 年前49 コメント

Show HN: Dynamic Resume Generator

github.com

7 ポイント投稿者 Mockapapella5 年前5 コメント

How to Program for 24 Hours Straight

quintenlisowe.medium.com

1 ポイント投稿者 Mockapapella5 年前0 コメント

Ask HN: Are there any audiobook services that exclusively use text to speech?

2 ポイント投稿者 Mockapapella5 年前1 コメント

Ask HN: What file formats do you use at work?

7 ポイント投稿者 Mockapapella6 年前9 コメント

5G, Transparent Solar Panels, and the Future of IoT

quintenlisowe.medium.com

1 ポイント投稿者 Mockapapella6 年前0 コメント

コメント

23 日前議論

    Location: Wisconsin
    Remote: Yes
    Willing to relocate: Yes (eager, for the right place and job)
    Technologies: RL Environments, FastAPI, Python, PyTorch, Kubernetes, Docker, AWS, MLOps
    Resume: https://drive.google.com/file/d/11LGN3YZj2uOJlTr8be77V44xVdGSr3kB
    Portfolio: https://thelisowe.com/
    Email: In profile or on resume

I'm an AI/ML Systems Engineer specializing in high-performance deployments. Most recently I've built RL environments for frontier labs like OpenAI and Google DeepMind. Previously I've built distributed AI classifier systems handling 30K QPS, developed a neural network for Rocket League gameplay, and created platforms that cut model deployment time from 6 months to 6 hours. Saved $500K/yr in infrastructure costs through optimization at previous role. Former technical founder with experience in humanoid robotics and AI writing assistance. I write about my projects and musings on my Substack: https://thelisowe.substack.com/

Seeking roles focusing on ML infrastructure, model optimization, post-training, or full-stack AI/ML systems engineering.

30 日前議論

Midwest Tech-adjacent companies. They’re so far behind on AI that they are functionally conservative about it.

先月議論

Hey HN, I made FunnyBench, a not-so-serious benchmark that lets you vote on which model tells the funniest jokes.

Many of these models are uncreative when it comes to joke diversity (often times telling the same joke over and over again), though there are a couple gems in there.

Benchmark details can be found at the bottom of the page, let me know what you think :)

先月議論

    Location: Wisconsin
    Remote: Yes
    Willing to relocate: Yes (eager, for the right place and job)
    Technologies: FastAPI, Python, PyTorch, Kubernetes, Docker, AWS, MLOps
    Resume: https://drive.google.com/file/d/1j2cLYZsxDr2ngjMhhmH0EPMFjb7CIMA0
    Portfolio: https://thelisowe.com/
    Email: In profile or on resume

AI/ML Systems Engineer specializing in high-performance deployments. Most recently I've built RL environments for frontier labs like OpenAI and Google DeepMind. Previously I've built distributed AI classifier systems handling 30K QPS, developed a neural network for Rocket League gameplay, and created platforms that cut model deployment time from 6 months to 6 hours. Saved $500K/yr in infrastructure costs through optimization at previous role. Former technical founder with experience in humanoid robotics and AI writing assistance. I write about my projects and musings on my Substack: https://thelisowe.substack.com/

Seeking roles focusing on ML infrastructure, model optimization, post-training, or full-stack AI/ML systems engineering.

5 か月前議論

I'm working on a TUI-based agent orchestrator called Tenex: https://github.com/Mockapapella/tenex

It's gone a long way to solve the "review" bottleneck people have been experiencing (though admittedly it doesn't fix all of it), and I'm in the process of adding support for Mac and Windows (WSL for now, native some other time).

Some of the features I've had for a while, like multi-project agent worktrees, have been added as a part of the Codex App, so it's good to see that this practice is proliferating because it makes it so much easier to manage the clusterf** that is managing 20+ agents at once without it.

I'm feeling the itch to have this working on mobile as well so I might prioritize that, and I'm planning to have a meta-agent that can talk to Tenex over some kind of API via tool calls so you can say things like "In project 2, spawn 5 agents, 2 codex, 2 claude, 1 kimi, use 5.2 and 5.4 for codex, use Opus for the claudes, and once kimi is finished launch 10 review agents on its code".

6 か月前議論

I have a small-medium following on Threads in the AI/tech space (~7K) and regularly post there, and have started posting on Twitter a little more recently, so I feel like I can provide some extra insight that might be missing in this thread.

The exposure to "what's what" in the tech space is clearly better on Twitter and it isn't even close. Nearly all tech news breaks on Twitter first, then flows downstream to Threads. For everything else it's kind of hard to say because I aggressively curate my social media feeds, so I don't get much content outside of my bubble.

The tech information I tend to get on Threads is more personal updates on mutuals' projects and niche eureka moments they have. There's maybe a dozen of these that I regularly see and interact with and maybe a couple dozen more that pop up occasionally. But again, this is after aggressively curating my feed and maintaining it for ~3 years.

I have a feeling that my efforts could have yielded better results on Twitter had I spent all that time posting and interacting there instead of Threads (or in addition to), hence me increasing my posting there.

7 か月前議論

https://github.com/Mockapapella/tenex

Tenex, a TUI for managing swarms of AI agents.

I noticed that as I'm using agents more and more my PRs are getting more ambitious (read: bigger diffs), and when I was reviewing them with agents I noticed that the first review wouldn't catch anything but the second would. This decreased my confidence in their capabilities, so I decided to make a tool to let me run 10 review agents at once, then aggregate their findings into a single agent to asses and address.

I was using Codex at the time, so Tenex is kind of a play on "10 Codex agents" and the "10x engineer" meme.

I've since added a lot of features and just today got to use it for the first time in a production system. Some rough edges for sure, but as I'm using it any time anything feels "off" or unintuitive I'm taking notes to improve it.

Fun fact, on my machine, while launching 50x Claude Code instances very nearly crashes it, I was able to launch 100x Codex instances no problem. I tried 500x but I ran into rate limits before they could all spawn :(

8 か月前議論

As far as I can tell they aren't

8 か月前議論

I wrote about something like this a couple months ago: https://thelisowe.substack.com/p/relentless-vibe-coding-part.... Even started building a little library to prove out the concept: https://github.com/Mockapapella/containment-chamber

Spoiler: there won't be a part 2, or if there is it will be with a different approach. I wrote a followup that summarizes my experiences trying this out in the real world on larger codebases: https://thelisowe.substack.com/p/reflections-on-relentless-v...

tl;dr I use a version of it in my codebases now, but the combination of LLM reward hacking and the long tail of verfiers in a language (some of which don't even exist! Like accurately detecting dead code in Python (vulture et. al can't reliably do this) or valid signatures for property-based tests) make this problem more complicated than it seems on the surface. It's not intractable, but you'd be writing many different language-specific libraries. And even then, with all of those verifiers in place, there's no guarantee that when working in different sized repos it will produce a consistent quality of code.

9 か月前議論

OK can someone smarter than me educate me?

A couple weeks ago I was curious what the strictest programming language was. ChatGPT listed a couple, and it kicked off a short discussion where I began asking it about the capabilities of stricter programming languages at low levels. Funny enough at the end it mentioned that SPARK/Ada was the strictest you could get at the lowest levels, same as Ironclad.

At one point while asking it about drivers, it said "ACL2’s logic is [...] side‑effect‑free definitions with termination proofs when admitted to the logic. That is misaligned with effectful, interrupt‑driven kernel code.

I'm not an OS or kernel dev, most of my work has been in Web Dev, ML, and a little bit of embedded. How accurate is the information that was presented to me? Here is the link to the discussion: https://chatgpt.com/share/691012a7-a06c-800f-9cc9-54a7c2c8b6...

I don't know SPARK or Ada, but it just bothers me to think that we can't...I guess...prove everything about our software before we run it (yes yes, I'm familiar with halting problem shenanigans, but other than that).

10 か月前議論

This sounds very similar to my workflow. Do you have pre-commits or CI beyond testing? I’ve started thinking about my codebase as an RL environment with the pre-commits as hyperparameters. It’s fascinating seeing what coding patterns emerge as a result.

12 か月前議論

> When it’s able to create code that compiles, the code is invariably inefficient and ugly.

Why not have static analysis tools on the other side of those generations that constrain how the LLM can write the code?

12 か月前議論

SEEKING WORK | Remote | AI Infrastructure & Performance

Location: Wisconsin

---

Last September I built an AI inference tool that hit #3 on HN (https://news.ycombinator.com/item?id=41620530). It processed 17.3M messages in 24 hours and only cost $17 to run.

I specialize in:

- LLM inference optimization (FastAPI, proper batching, memory management)

- CI/CD pipelines for ML deployments

- Making AI systems cost-effective at scale

Recent work: FrankenClaude (reasoning injection experiments, https://thelisowe.substack.com/p/frankenclaude-injecting-dee...), self driving Rocket League (https://thelisowe.substack.com/p/building-an-ai-that-plays-r...), diffdev (AI-powered code modification tool, https://pypi.org/project/diffdev/).

Previously at Sprout Social where I built their ML inference platform - reduced deployment time from 6 months to 6 hours and cut AWS costs by $500K/yr.

Looking for interesting problems in AI infrastructure, performance optimization, or building products from scratch.

Tech: PyTorch, FastAPI, K8s, Docker, AWS, ONNX

---

Resume: https://drive.google.com/file/d/1qO8XdisNTFq_wmrQGDKnu6eWDi2... GitHub: github.com/Mockapapella Blog: thelisowe.substack.com

Contact: My email is in my bio or on my resume

12 か月前議論

    Location: Wisconsin
    Remote: Yes
    Willing to relocate: Yes
    Technologies: Python, PyTorch, Kubernetes, Docker, AWS, FastAPI, ONNX, MLOps
    Resume: https://drive.google.com/file/d/1qO8XdisNTFq_wmrQGDKnu6eWDi2g33Me/view
    GitHub: https://github.com/Mockapapella
    Email: In profile or on resume

AI/ML Engineer specializing in high-performance deployments. Built distributed systems handling 30K QPS, developed a neural network for Rocket League gameplay, and created platforms that cut model deployment time from 6 months to 6 hours. Saved $500K/yr in infrastructure costs through optimization at previous role. Former technical founder with experience in humanoid robotics and AI writing assistance. I write about my projects and musings on my blog: https://thelisowe.substack.com/

Seeking roles focusing on ML infrastructure, model optimization, post-training, or full-stack AI engineering.

昨年議論

SEEKING WORK | Remote | AI Infrastructure & Performance

Location: Wisconsin

---

Last September I built an AI inference tool that hit #3 on HN (https://news.ycombinator.com/item?id=41620530). It processed 17.3M messages in 24 hours and only cost $17 to run.

I specialize in:

- LLM inference optimization (FastAPI, proper batching, memory management)

- CI/CD pipelines for ML deployments

- Making AI systems cost-effective at scale

Recent work: FrankenClaude (reasoning injection experiments, https://thelisowe.substack.com/p/frankenclaude-injecting-dee...), self driving Rocket League (https://thelisowe.substack.com/p/building-an-ai-that-plays-r...), diffdev (AI-powered code modification tool, https://pypi.org/project/diffdev/).

Previously at Sprout Social where I built their ML inference platform - reduced deployment time from 6 months to 6 hours and cut AWS costs by $500K/yr.

Looking for interesting problems in AI infrastructure, performance optimization, or building products from scratch.

Tech: PyTorch, FastAPI, K8s, Docker, AWS, ONNX

---

Resume: https://drive.google.com/file/d/1qO8XdisNTFq_wmrQGDKnu6eWDi2... GitHub: github.com/Mockapapella Blog: thelisowe.substack.com

Contact: My email is in my bio or on my resume

昨年議論

    Location: Wisconsin
    Remote: Yes
    Willing to relocate: Yes
    Technologies: Python, PyTorch, Kubernetes, Docker, AWS, FastAPI, ONNX, MLOps
    Resume: https://drive.google.com/file/d/1qO8XdisNTFq_wmrQGDKnu6eWDi2g33Me/view
    GitHub: https://github.com/Mockapapella
    Email: In profile or on resume

AI/ML Engineer specializing in high-performance deployments. Built distributed systems handling 30K QPS, developed a neural network for Rocket League gameplay, and created platforms that cut model deployment time from 6 months to 6 hours. Saved $500K/yr in infrastructure costs through optimization at previous role. Former technical founder with experience in humanoid robotics and AI writing assistance. I write about my projects and musings on my blog: https://thelisowe.substack.com/

Seeking roles focusing on ML infrastructure, model optimization, post-training, or full-stack AI engineering.

昨年議論

This is a good article on the "fog of war" for GPU inference. Modal has been doing a great job of aggregating and disseminating info on how to think about high quality AI inference. Learned some fun stuff -- thanks for posting it.

> the majority of organizations achieve less than 70% GPU Allocation Utilization when running at peak demand — to say nothing of aggregate utilization. This is true even of sophisticated players, like the former Banana serverless GPU platform, which operated at an aggregate utilization of around 20%.

Saw this sort of thing at my last job. Was very frustrating pointing this out to people only for them to respond with ¯\_(ツ)_/¯. I posted a much less tactful article (read: rant) than the one by Modal, but I think it still touches on a lot of the little things you need to consider when deploying AI models: https://thelisowe.substack.com/p/you-suck-at-deploying-ai-mo...

昨年議論

Holy shit I think that might be it! I have been looking for that tweet for like a year now. Thanks!

昨年議論

Honestly I thought you guys had launched already (and didn't know you were a part of YC), been aware of you guys for years now it seems. Congrats on the launch! Hope the twitter issues aren't causing you guys too many problems.

Normally I'd send this as a DM or email, but I think it could be useful for others to learn about how to use your service/the limitations of it. A couple weeks ago I made a search for:

    In early 2023, Andrej Karpathy said something like "large training runs are a good test of the overall health of the network." Something something resilience as well I think. I need you to find it.

Unfortunately it wasn't able to find it, but it was either in a tweet or a really long presentation, neither of which are good targets for search. It was around the same time that this (https://www.youtube.com/watch?v=c3b-JASoPi0) video was posted, like within a couple weeks before or after. How could I have improved my query? Does exa work over videos?

昨年議論

Thanks, looks interesting. Sent an email