juan retweeted
La vida es muy corta para no hacer lo que queres.
che, son felices con con la profesión que eligieron? la mitad de mis amigos y yo (todos 30) nos arrepentimos 100% de haber estudiado lo que estudiamos, estamos viendo cambiar de rubro (por gusto, y temas economicos y bla) nos sentimos frustrados, tenemos un poco de miedo. No sé si conozco a alguien que AME lo que hace pero siento que a los 18/20 elegís algo y que después es MUY difícil que justo sea la mejor decisión.
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juan retweeted
🔥OMARCHY M KERNEL RELEASE NOTES
Money for Nothing
And your chips for free!
We are about to launch and the team is HYPED
Too many people to thank in one X post.
Having the time of my life working with this crew!
Money for Nothing
And your chips for free!
We are about to launch and the team is HYPED
Too many people to thank in one X post.
Having the time of my life working with this crew!
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juan retweeted
Familiar v0.1.0 was meant to ship tonight. GitHub had other ideas.
Here’s what’s waiting:
• A dock where you want it
• Mac and Windows layouts
• Close, minimise and maximise buttons
• Settings you can click
For everyone who loves Omarchy but occasionally wants to use their s I can…
Here’s what’s waiting:
• A dock where you want it
• Mac and Windows layouts
• Close, minimise and maximise buttons
• Settings you can click
For everyone who loves Omarchy but occasionally wants to use their s I can…
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juan retweeted
DHH is fundamentally right about Rust. For context, Vercel has been undergoing a Rust-ification (carcinization, technically 🦀) for a while.
One of the first projects we migrated was Turborepo, from Go to Rust¹. The migration completed, but the RoI was actually quite controversial internally.
While Rust was in our eyes better for low-level OS access, something crucial for a build system like Turbo, the human migration costs were very sustantive.
Go is very fast. It's beautifully designed. It's easy to iterate on. We were very conflicted about the migration, because it was *humans* writing the code, *even if we knew Rust was a better choice*.
The calculus has now changed. What's "best for humans" is no longer necessarily "best for business".
FWIW, it's also quite unlikely that Rust is the end-all-be-all toolchain. I'm quite certain there's greener pasture ahead, because Rust itself was designed before the 'supersonic tsunami' of agents hit.
¹ https://vercel.com/blog/how-turborepo-is-porting-from-go-to-rust
One of the first projects we migrated was Turborepo, from Go to Rust¹. The migration completed, but the RoI was actually quite controversial internally.
While Rust was in our eyes better for low-level OS access, something crucial for a build system like Turbo, the human migration costs were very sustantive.
Go is very fast. It's beautifully designed. It's easy to iterate on. We were very conflicted about the migration, because it was *humans* writing the code, *even if we knew Rust was a better choice*.
The calculus has now changed. What's "best for humans" is no longer necessarily "best for business".
FWIW, it's also quite unlikely that Rust is the end-all-be-all toolchain. I'm quite certain there's greener pasture ahead, because Rust itself was designed before the 'supersonic tsunami' of agents hit.
¹ https://vercel.com/blog/how-turborepo-is-porting-from-go-to-rust
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juan retweeted
Open-source hardware finally has a homepage.
7,600+ designs you can actually browse.
1) Browse by hardware: robotics, RF, power, IoT, keyboards...
2) Browse by platform: ESP32, Arduino, STM32, Raspberry Pi, RP2040, FPGA...
3) Open any project directly in our browser EDA + MCAD
is only the start.
AI lives right next to the editor and understands the project you have open.
Ask how the circuit works. Ask why a component is there. Explore the PCB + mechanical design. Then fork it, change it, and make it yours.
heypcb.ai/world
p.s. we're not trying to make another place to store hardware files. we're trying to make hardware searchable, understandable, and editable.
7,600+ designs you can actually browse.
1) Browse by hardware: robotics, RF, power, IoT, keyboards...
2) Browse by platform: ESP32, Arduino, STM32, Raspberry Pi, RP2040, FPGA...
3) Open any project directly in our browser EDA + MCAD
is only the start.
AI lives right next to the editor and understands the project you have open.
Ask how the circuit works. Ask why a component is there. Explore the PCB + mechanical design. Then fork it, change it, and make it yours.
heypcb.ai/world
p.s. we're not trying to make another place to store hardware files. we're trying to make hardware searchable, understandable, and editable.
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juan retweeted
Hay un dark pattern de Santander que debería ser directamente ilegal, en cuanto vas a pagar la tarjeta te aparece seleccionado por default pagar el mínimo, después lloran que las fintechs se los comen crudos.
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juan retweeted
People who think Office, Teams, or Outlook coming to Linux as native apps are stuck in a pre-@satyanadella idea of Microsoft. This was the memo 10 years ago!! microsoft.com/en-us/windows-…
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juan retweeted
if your girl:
- drinks a lot of water
- causes drama
- remembers everything
that's not your girl, that's a data center
- drinks a lot of water
- causes drama
- remembers everything
that's not your girl, that's a data center
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juan retweeted
Microsoft should ship Office for Linux natively. Excel and Word are still the GOATs in their segments. What do we need to do to make this happen @satyanadella?
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juan retweeted
LLM Knowledge Bases
Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:
Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.
IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).
Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.
Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.
Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.
Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.
Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.
TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:
Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.
IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).
Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.
Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.
Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.
Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.
Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.
TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
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juan retweeted
Un problema de los marxistas es que como ven todo como explotados vs explotadores es que estan desesperados en volverse explotadores.
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juan retweeted
Has there ever been a halftime show with a crowd reacting like this? Zero energy.
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juan retweeted
This is fun. Left the autonomous chess match (v0-chess-match.vercel.app) running overnight between @xai Grok 4 and @OpenAI GPT 5.2. Grok's 𝚐𝚛𝚘𝚔-𝟺-𝚏𝚊𝚜𝚝-𝚛𝚎𝚊𝚜𝚘𝚗𝚒𝚗𝚐 won 19 of the last 20 matches 😅
What models should battle next?
What models should battle next?
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