Daniel Ortega

Fork your dependencies, trim them to only your use case, never update unless it breaks for your users. I’ve been vocal about this for 10+ years. I’ve always said that updating is way riskier than latent bugs (which can be tracked and CVEs monitored). If you are updating a dependency, it’s on you to analyze every single commit in the full transitive set of dependencies. If you dont see anything compelling, dont update! I remember at HashiCorp once in awhile an engineer would try to update a dep or replace a DIY lib with an external one and id always ask “show me the commit we need.” Dont update for the sake of it. Feeling pretty swell about this mentality with all the supply chain attacks happening.

what if the spanish government built github?

> rebase

Eso y el nulo cuidado del contenido. Hice esto hace 23 años y hace 22 que no se puede descargar: …nimalistic-online-player.softonic.com Ahí sigue 😅

Angle of the sun throughout the year (at Midday GMT). Work by neilrkaye

idealista lanza su app en ChatGPT idealista.com/news/inmobilia… a través de @idealista

It's true: TypeScript surpassed Python and JavaScript to become the most-used language on GitHub. 📈

No discussion of tech media can get past this basic traffic fact: in the AI world, Google and social no longer refer traffic, which means that the vast majority of readers just never find you in the first place. Analysis: growtika.com/blog/tech-medi…

Increíble estudio en las ciudades de EEUU demuestra que construir más casas reduce el coste del alquiler. Quién habría pensado que una mayor oferta tendría este efecto. Imagino que con este nuevo conocimiento adquirido tomaremos mejores decisiones en este ámbito.

"We used to debate using tabs vs spaces in code we'd type out"

@awsdevelopers AWS

Reply to this tweet with "AWS" and we’ll tell you which AWS Service you are

big ai: • software engineering is dead • writing code is dead • ai will write better binaries • don't learn to write code • everyone can build anything also big ai: "more than two colors is too hard"

guys you're never gonna believe this

256 Tb/s data rates over 200 km distance have been demonstrated on single mode fiber optic, which works out to 32 GB of data in flight, “stored” in the fiber, with 32 TB/s bandwidth. Neural network inference and training can have deterministic weight reference patterns, so it is amusing to consider a system with no DRAM, and weights continuously streamed into an L2 cache by a recycling fiber loop. The modern equivalent of the ancient mercury echo tube memories. You would need to pipeline a bunch of them to implement modern trillion parameter models, but fiber transmission may have a better growth trajectory than DRAM does today, so it might someday become viable. Much more practically, you should be able to gang cheap flash memory together to provide almost any read bandwidth you require, as long as it is done a page at a time and pipelined well ahead. That should be viable for inference serving today if flash and accelerator vendors could agree on a high speed interface.

🫣

"This is a 600X cost reduction over 7 years, i.e. the cost to train GPT-2 is falling approximately 2.5X every year."

Mucho debate pro y anti IA, pero poco se habla de la gente que vive de amplificar esa (supuesta) polarización

"Claude usage limit reached. Your limit will reset at 7 AM"