Super Villain Ops

SWE | MLE | AI automation | Software Services

[Science Simplified @Scivf4]

This is what the AI brain looks like.

[Bryan Johnson @bryan_johnson]

This is it.
Everything learned spending millions on longevity.

From: Your Immortal Unc and Auntie.
To: Our Immortal nieces and nephews.

0. Sleep is the world's most powerful drug.
1. Be in your bed for 8 hours
2. Same bedtime every night, any time before midnight
3. Don’t eat right before bed
4. Calm foods for dinner
5. No screens 1 hour before bed
6. Avoid added sugar (be aware it’s in everything)
7. Avoid all things in an American convenience store
8. Avoid fried foods
9. Shoes off at the door
10. Eat whole foods, particularly veggies fruits nuts legumes berries
11. Walk a little after meals or air squats
12. Get your heart rate high routinely
13. Lift heavy things
14. Stretch daily
15. Water pik, floss, brush, tongue scrape, morning and night
16. Make an effort to drink water
17. Get sunlight when you wake up (UV is low)
18. Protect skin in midday sun
19. Stand up straight
20. See at least one friend once a week
21. Avoid plastic where you can (in all things)
22. Circulate air in rooms
23. When stressed, breathe, learn to calm your body
24. Go to the dentist
25. Avoid sitting for long times
26. Protect your hearing, the world is too loud
27. Alcohol is bad for you
28. Finish coffee before noon
29. Avoid bright lights after sunset
30. If obese, look into a GLP
31. Sleep in a cold room
32. Texting while driving is dangerous
33. Turn off all notifications
34. Limit social media use
35. Don’t smoke anything
36. If you struggle to sleep, read a physical book before bed
37. 1 hour before bed have a calm wind down routine: bath, read, light walk, listen to music
38. The body is a clock and loves routine. Have a daily morning and evening schedule.
39. Avoid long distance travel where you can
40. Baby steps first: incorporate new things slowly
41. Do less… most things don’t work.

Bonus points if you get your blood checked.

Start here, it will change your life.

[📎 @Iithosphere]

read this and immediately got out of bed

[blue @bluewmist]

To my 25 - 35 year olds, you have reached the age where people around you are starting to give up on themselves because they think it's too late. don't let that energy rub off on you. It's not too late.

[TDM (e/λ) (L8 vibe coder 💫) @cto_junior]

(Manager watching their team on the verge of mass resignation from back to back quarters of impossible deliverables with unrealistic deadlines):

Hey! Let's have a hackathon!

[trish @TrisH0x2A]

C + memory management = Systems Programming
C + networking + sockets = Backend Engineering
C + OS concepts + scheduling = Kernel Development
C + data structures + allocators = Runtime Design
C + profiling + benchmarks = Performance Engineering
C can literally make you dangerous

[Mari @Tech_girlll]

Software Engineers going back to Documentation after reaching their Claude limit.

[roon @tszzl]

say it with me now. experts are fake, smart generalists rule the world, everything is designed by people no smarter than you, and courage is in shorter supply than genius

[Noctara @rumilyrics]

You think it takes time, but it actually takes alignment.

[Khushi🪐 @_khusheyyy]

i feel like all roads lead to

mathematics
physics
philosophy

[chiefofautism @chiefofautism]

someone made the most ADDICTIVE game to learn DATA CENTER networking

its called Data Center, $6 game, you start with bare floors, buy racks, mount servers, route every cable by hand

the INSANE part, every customers traffic shows as colored balls rolling through your cables... you literally see bottlenecks in real time

180 reviews in 48 hours, people with RTX 4090 rigs are HOOKED on a $6 cabling sim

[darkzodchi @zodchiii]

https://t.co/7TIGxBpQt5

[Andrej Karpathy @karpathy]

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.

[Kai @hqmank]

If you're using Claude Code, this is worth knowing.

Instead of worrying about whether Opus 4.6 or GPT 5.4 is better, it's more useful to combine them in the same workflow.

OpenAI shipped an official Claude Code plugin called codex-plugin-cc. You can now call Codex directly from inside Claude Code.

Three commands:

/codex:review
Code review on uncommitted changes or diffs against a branch. Read-only.

/codex:adversarial-review
Challenges your design decisions, not just syntax.
"Why this caching strategy?"
"Race condition here?"
Append free-form text to steer the review.

/codex:rescue
Hands the task to Codex when Claude gets stuck.
Supports --resume to continue from the last run.

Adversarial review is the killer feature. Especially before shipping auth changes, infra scripts, or anything involving data loss.

There's also a review gate: Codex auto-reviews every time Claude finishes and blocks completion if issues are found.

Claude writes, Codex reviews.

https://t.co/Rulz8kXOx5

[Arya Hezarkhani @_i_am_arya]

Today, we're announcing Heaviside, our foundation model for electromagnetism.

Trained on tens of millions of designs and over 20 years of proprietary simulation data, Heaviside predicts electromagnetic behavior from geometry in 13ms, which is 800,000x faster than a commercial solver.

Heaviside is not a language model, and it’s not a surrogate model. Heaviside marks a new class of foundation model for physics which understands the fundamental relationships between materials, the geometries and the electromagnetic fields they generate.

We’re releasing a research preview of Heaviside in Atlas RF Studio, an interactive agentic sandbox where you describe the EM behavior you want and the model generates the physical structure that produces it.

@arenaphysica , we believe the implications of this class of model extend well beyond RF, as the frontier of exquisite hardware is electromagnetically-governed: wireless communication, radar, power delivery, high-speed computing, and the interconnects inside every chip on earth.

In the months ahead, we’re excited to scale up Heaviside to broader frequency ranges, design spaces, and to support silicon-level designs, and deploy it with our closest partners and collaborators in service of their biggest design challenges.

If you’ve read our thesis, this is just Step 2 in our pursuit of electromagnetic superintelligence.

Read the full announcement and try Atlas RF Studio…tell us what you think: https://t.co/oCOsJQvF1h

[clem 🤗 @ClementDelangue]

After @Pinterest @Airbnb @NotionHQ @cursor_ai, today it’s @eoghan @intercom publicly sharing that they’re finding it better, cheaper, faster to use and train open models themselves rather than use APIs for many tasks.

And hundreds of other companies are doing the same without sharing.

Ultimately, I believe the majority of AI workflows will be in-house based on open-source (vs API). It took much more time than we anticipated but it’s happening now!

[Will Manidis @WillManidis]

https://t.co/bbApQIwYr5