justinl33

So nice to see GPU's being used for classical reasons again.

This is good. PR review has been completed neglected basically from day 0.

Did some self-research on Reddit about why (https://www.reddit.com/r/github/comments/1gtxqy6/comment/lxv...)

entity-oriented models are more stable over time compared to procedure-oriented RPC. In my experience, starting with resources/entities and mapping operations to them does lead to cleaner APIs that are easier to evolve.

I disagree that OpenAPI is just RPC mapped to HTTP. A well-designed OpenAPI spec can be quite RESTful. The problem is many developers don't take the time to design good resource models and just slap RPC-style operations into URL paths.

> This is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT.

This is a noteworthy achievement.

I've built similar systems using Apache Airflow and Temporal, but the complexity was overwhelming. Using simple maps with enter/leave phases for workflow steps is much cleaner than dealing with DAG frameworks.

It's giving _Students for Fair Admissions v. Harvard_

human stereo vision processing has incredibly sophisticated noise filtering capabilities that are still hard to replicate in software. The shimmering effect people report is essentially your visual cortex highlighting areas where the stereo correspondence fails.

let's thank him for making some absolutely god-mode architectural calls (protocol layering, manufacturer agnosticism, etc.)

this is good. the automated systems were getting increasingly byzantine, with layers of rules trying to patch edge cases, which just created more edge cases.

I've maintained several SDKs, and the 'cover everything' approach leads to nightmare dependency trees and documentation bloat. imo, the LLM paradigm shifts this even further - why maintain a massive SDK when users can generate precisely what they need? This could fundamentally change how we think about API distribution.

Exactly

nice, thanks for sharing!

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I strongly believe that the addition of every metric brings with it an associated productivity tax in the form of: 1. time spent doing things that exploit this metric 2. time spent purely documenting/surfacing this metric on your ‘brand’

> The guardrails are made of laws, not morals

damn.

The wall is a red herring 2. why was a commercial airliner attempting a no-gear belly landing with full fuel load on a runway that's only 2,800m, rather than divert to Incheon?

Python's module loading mechanism is thread-safe by default, and module-level variables are effectively singletons. This would suffice:

_connection = None

def get_connection(): global _connection if _connection is None: _connection = create_connection() return _connection

a simple docker run --memory=512m --cpus=1 could have at least limited the damage here.

this research actually demonstrates that AI will reduce the phishing threat long-term, not increase it. Yes, the 50x cost reduction is scary, but it also completely commoditizes the attack vector.

you need to negotiate a proper shareholders agreement for that 1% with specific anti-dilution provisions and tag-along rights. The military pivot actually gives you leverage - defence contracts often require stable cap tables and background checks on all shareholders.

historically, maritime navigation has always required much higher precision since a small error compounds into missing an island entirely. land-based measurements could afford to be fuzzier because you can course-correct using visible landmarks. this is also why maritime measurements standardized globally much earlier than land measurements.

the real technical achievement here is maintaining consistent camera sensitivity over 4 years. Most consumer CMOS sensors degrade significantly from UV exposure over that timeframe, which would show up as a gradual darkening in the composite

The comparison to weather satellites is misleading and oversells this technology. Weather radar works by detecting water droplets at known atmospheric heights - it's a fundamentally different problem than trying to measure glucose concentrations in blood through layers of tissue. The real breakthrough here isn't the radar tech (which has existed for years), it's the machine learning pipeline that can extract meaningful glucose data from extremely noisy radar returns.

yeah, authentication complexity with WebSockets is severely underappreciated. We ran into major RBAC headaches when clients needed to switch between different privilege contexts mid-session. Long polling with standard HTTP auth patterns eliminates this entire class of problems.

It’s okay Terence, it happens to the best of us.

I've seen people pay a yearly subscription for much less, so I'm not going to comment