ironfootnz's comments

I think the only folks can solve this at the guys at autohand.ai They're into something at this level.

@Sean you can try this https://x.com/unslothai/status/1956027720288366883?s=46&t=Bo...

That’s my policy too. I see way too many Jenkins/Actions scripts with big logic blocks jammed into YAML. If the entire build and test process is just a single script call, we can run it locally, in a GitHub workflow, or anywhere else. Makes it less painful to switch CI systems, and devs can debug easily without pushing blind commits. It’s surprising how many teams don’t realize local testing alone saves huge amounts of time.

I’ve seen many teams get stuck when they rely too heavily on GitHub Actions’ magic. The key issue is how tightly your build logic and config become tied to one CI tool. If the declarative YAML gets too big and tries to handle complex branching or monorepos, it devolves into a maintenance headache—especially when you can’t test it locally and must push blind changes just to see what happens.

A healthier workflow is to keep all the logic (build, test, deploy) in portable scripts and let the CI only orchestrate each script as a single step. It’s easier to troubleshoot, possible to run everything on a dev machine, and simpler if you ever migrate away from GitHub.

For monorepos, required checks are maddening. This should be a first-class feature where CI can dynamically mark which checks apply on a PR, then require only those. Otherwise, you do hacky “no-op” jobs or you force your entire pipeline to run every time.

In short, GitHub Actions can be powerful for smaller codebases or straightforward pipelines, but if your repo is big and you want advanced control, it starts to feel like you’re fighting the tool. If there’s no sign that GitHub wants to address these issues, it’s totally reasonable to look elsewhere or build your own thin orchestration on top of more flexible CI runners.

I'm a big fan of their papers, this one didn't disappoint

L0L, this is basically OpenAI spec function calls with a different semantics.

New fear unlocked!!! I will no longer look at my phone the same way!

Over the next five years, intelligent code assistants like GitHub Copilot will significantly transform software development by automating repetitive tasks and enhancing developer productivity. These tools, built on advancements in AI and large language models (LLMs), will help developers focus more on problem-solving and less on manual coding. While LLMs won’t replace developers, they will streamline workflows, making programming more accessible globally, especially with innovations in personalized models and natural language interfaces. The future of coding may evolve into a collaborative process between humans and AI, reshaping the development landscape.

What a useless way of proposing something to the web. robots.txt is the way to go to anyone on the web.

I think this is a very useful framework to write about on Wikipedia on how not to use HTML in your python code. There's a canonical reason on why we shouldn't.

Readability, reusability ... the list goes on.

Sorry but this will collapse in the next 18 months.

Sam is willing to lie to become relevant to AI history. What's his doing is just creating a crumble to his own ruin. - GeoHortz

$1.3B reduced to $420M.

I thought the tax were already paid and the money was clean. Far out!

Sundar Pichai, CEO, said: “Our results in the first quarter reflect strong performance from Search, YouTube and Cloud. We are well under way with our Gemini era and there’s great momentum across the company. Our leadership in AI research and infrastructure, and our global product footprint, position us well for the next wave of AI innovation.”

The only one mention, it's strong position across conservative approach. A must have for any large multi national company like Google.

I've been saying this for the last 10 years. Middle managers are Bozos. They don't let you get shit done and don't let you flush your ideas and brainstorm to products. I hope this tendency becomes the new norm.

To me, it doesn't make more sense the price for the common stock for NVDA, a 66 p/e ratio for NVDA is anticipating them beating earnings handily for the next decade.

This is to think that AMD or any cloud provider wouldn't compute against them in this meantime.

But what really put me off, was their balance sheet, a slow town of roughly 18% in the output pace compared to the previous quarters is staggering. Which means they have at least 5 to 6 months of leading time before other vendor catch-up.

Enough for me, it was a fun ride since 2017.

RIP my friend.

It's a good article for people with no strong background in NLP or LLMs. Gives a comprehensive overview on how this could be applicable for startups.

Enterprise, not so quite, there are a lot of other stuff to consider like points missing, ethical application, filtering, security, points that are very important for enterprise customers.

Also, in-context learning is just one way to apply LLMs, there are way more applications like few short learning, fine tuning, depending on cost and application involved as I've highlighted here

https://twitter.com/igorcosta/status/1671316499179667456

the UI is quite limited, check out the outputs https://play.vercel.ai/r/I25tmJs when needs a scroll bar.

He's finally catching up with me. I wrote an article about that a few months ago.

https://twitter.com/igorcosta/status/1612704076315389952