enjoylife

I agree there will always be a need for community. Human connections. Folks experiencing the tastes, smells, etc.

Eg from your share, the model output “I prefer it Scotch-heavy rather than 50/50 because otherwise it gets very sweet.”

It’s basically cribbed this from someone; it literally can’t taste.

The frontier labs are not yet able to do rlvr over mixology/meatspace. So maybe think on how to capitalize on that.

Another restatement of Brandolini’s Law. The cost of parroting this kind of information is very low, while the cost of refuting it is very high. And the value an outlet can extract from its readership to fund that refutation is nowhere close to cover its outlay. Maybe a counter is the occasional take-down article can sometimes go more viral than the original claim, but chasing those is probably unprofitable too.

One thought is once it can touch the underlying system, it can provision resources, spawn processes, and persist itself, crossing the line from tool to autonomous entity. I admit you could do that in a browser shell nowadays, just maybe with more restrictions and guardrails. I don’t have any strong opinions here, but I do think a lower cost to escape the walled gardens agi starts in will be a factor

> It is not yet optimized for desktop OS-level control

Alas, AGI is not yet here. But I feel like if this OS-level of control was good enough, and the cost of the LLM in the loop wasn't bad, maybe that would be enough to kick start something akin to AGI.

Officers are a limited resource, so their deployment matters. Are they assigned to areas that most benefit citizens, or those that most benefit the city? Is the focus on maximizing ticket revenue, or addressing the most dangerous violations, like blocked bike lanes? Are they primarily a revenue tool, a public safety measure, or just extra eyes on the street? Do wealthier neighborhoods receive more enforcement, effectively buying themselves safer streets? Basically I'm wondering does parking enforcement benefit SF residents uniformly?

Officer 0336 is raking it in for the city. I wonder if there is a correlation with the areas which generate a lot of tickets and other city datasets. Perhaps crime rate or average household income?

Interesting but cherry picked excerpts. Show me more, e.g. a distribution over various temp or top_p.

I think we’ve always shaped ourselves based what we’re capable of building. Think of how infrastructure such as buildings and roadways shape our lives within them. What I do agree with you, is how LLMs are shaping our mental thought how we are offloading a lot of our mental capacities with blind trust in the LLM output.

I agree that these visuals rarely drive decisions on their own. They’re more like supporting tools… useful for framing an argument or guiding a narrative in a presentation, especially when static.

Unless it’s interactive or tied to live data the usefulness of the visualization produced is limited.. it’s a shame and it’s something this tools should pivot towards

> Once concepts are selected, Sphere walks the knowledge graph and generates SQL queries to retrieve data from the warehouse, no manual joins or technical mediation required.

If I had to guess this is how eng pitched it to the business to carve out the time to build this tooling. As with all these internally built schemas, ui’s, tooling, etc… they’re never gonna post how much this is actually used relative to the work arounds ds and eng use in their day to day.

Maybe some reverse attribution to determine how much credit to give different sources in the old top k results. Maybe a llm that is trained on fact finding, and giving proper % of credit. But this would probably require a collective contract in place to benefit site owners, which means its never going to happen.

> these tools are not only useful for writing the final code

This sparked a thought in how a large part of the job is often the work needed to demonstrate impact. I think this aspect is often overlooked by some of the good engineers not yet taking advantage of the AI tooling. LLM loops may not yet be good enough to produce shippable code by themselves, but they sure are capable to help reduce the overhead of these up and out communicative tasks.

“‘It’s still learning’ is a misnomer. The model isn’t learning—we are. LLMs are static after training; all improvement comes from human iteration in the outer loop: fine-tuning, prompt engineering, tool integration, retrieval. Until the outer loop itself becomes autonomous and self-improving, we’re nowhere near AGI. Current hype confuses capability with agency.

Was going to point this out too. One suggestion would be to try this on libraries having recent major semvar bumps. See if the compressed docs do better on the backwards incompatible changes.