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adam_patarino

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CEO and Co-Founder of Rig.ai 2x Founder and Software Engineer Living in Indianapolis

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Claude Opus 4.8 2 months ago

Smaller models can already outperform SOTA and massive models on specific tasks / domains.

Every software developer knows that you can’t make projects go faster just by typing faster. If that were the case we would all be taking typing lessons.

So well said.

AI is unveiling how the bureaucracy is the slow part.

I’ve been talking to a lot of engineers about how they use AI in their day to day and it’s dramatically different than what you see from the hypers.

The vast majority use one agent at a time and careful step through code. The main benefit they report is often about researching the codebase and possible solutions.

Gusto makes payroll zero clicks.

I’ve used xero and quickbooks and they integrate with many banks and expense management platforms to automate closing.

Closing books and running payroll feel like solved problems with today’s saas and high stakes if you mess up.

This is one of those areas I would spend more time checking the outputs than it would take me to click the button myself.

DeepSeek v4 3 months ago

Prices are not just hard cost of inference. Training costs are not equal. Chinese labs have cheaper access to large data centers. I also suspect they operate far more efficiently than orgs like openAI.

Discusses the difference between training for one-shot / one turn coding tasks and agentic long horizon agentic coding workflows.

Actually, we're more broadly trained than most models. We did long tail training across languages, so we improved execution with languages like java, swift, and even cobol.

It's definitely a david vs goliath. But we know there's a subset of devs who need the privacy or unlimited nature of local.

It’s not a query / prompt thing though is it? No matter the input LLMs rely on some degree of random. That’s what makes them what they are. We are just trying to force them into deterministic execution which goes against their nature.