Saphyl

[Argona @Argona0x]

i gave an AI $50 and told it "pay for yourself or you die"

48 hours later it turned $50 into $2,980

and it's still alive

autonomous trading agent on polymarket

every 10 minutes it:

→ scans 500-1000 markets
→ builds fair value estimate with claude
→ finds mispricing > 8%
→ calculates position size (kelly criterion, max 6% bankroll)
→ executes
→ pays its own API bill from profits

if balance hits $0, the agent dies

so it learned to survive

built in rust for speed

claude API for reasoning (agent pays for its own inference)

runs on a $4.5/month VPS

weather markets: parses NOAA before polymarket updates sports: scrapes injury reports, finds mispricing crypto: on-chain metrics + sentiment

$50 → $2,980 in 48 hours

how much do u think i’ll see in a week?

[The Humanoid Hub @TheHumanoidHub]

New humanoid startup out of Hungary: Allonic

The unique hand design moves away from rigid industrial design toward a biomimetic approach.

- Each finger is braided around like a rope in a single autonomous process; this process also includes the threading of the tendons through the braided "tissues."

- The braidings wrap around and provide distributed load across the surface of the finger rather than a single point of failure.

- The hand naturally conforms to objects during a grip and allows for variable stiffness like human hands.

The cost of the hand (not including motors) could be as low as $50.

[NVIDIA AI Developer @NVIDIAAIDev]

This is an incredible performance breakthrough from @UnslothAI.

12x faster fine-tuning, 35% less VRAM, all with no loss in accuracy enables fine-tuning of MoE models like gpt-oss-20b on just 16 GB of VRAM.

[Claude @claudeai]

Introducing Claude Opus 4.6. Our smartest model got an upgrade.

Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes.

It’s also our first Opus-class model with 1M token context in beta.

[Brendan Carr @BrendanCarrFCC]

The FCC welcomes and now seeks comment on the SpaceX application for Orbital Data Centers.

The proposed system would serve as a first step towards becoming a Kardashev II-level civilization and serve other purposes, according to the applicant.

[Claude @claudeai]

Claude is built to be a genuinely helpful assistant for work and for deep thinking.

Advertising would be incompatible with that vision.

Read why Claude will remain ad-free: https://t.co/Dr8FOJxINC

[Claude @claudeai]

Ads are coming to AI. But not to Claude. Keep thinking.

[The Kobeissi Letter @KobeissiLetter]

BREAKING: SpaceX has acquired xAI.

[Andrej Karpathy @karpathy]

nanochat can now train GPT-2 grade LLM for <<$100 (~$73, 3 hours on a single 8XH100 node).

GPT-2 is just my favorite LLM because it's the first time the LLM stack comes together in a recognizably modern form. So it has become a bit of a weird & lasting obsession of mine to train a model to GPT-2 capability but for much cheaper, with the benefit of ~7 years of progress. In particular, I suspected it should be possible today to train one for <<$100.

Originally in 2019, GPT-2 was trained by OpenAI on 32 TPU v3 chips for 168 hours (7 days), with $8/hour/TPUv3 back then, for a total cost of approx. $43K. It achieves 0.256525 CORE score, which is an ensemble metric introduced in the DCLM paper over 22 evaluations like ARC/MMLU/etc.

As of the last few improvements merged into nanochat (many of them originating in modded-nanogpt repo), I can now reach a higher CORE score in 3.04 hours (~$73) on a single 8XH100 node. This is a 600X cost reduction over 7 years, i.e. the cost to train GPT-2 is falling approximately 2.5X every year. I think this is likely an underestimate because I am still finding more improvements relatively regularly and I have a backlog of more ideas to try.

A longer post with a lot of the detail of the optimizations involved and pointers on how to reproduce are here:
https://t.co/vhnK0d3L7B
Inspired by modded-nanogpt, I also created a leaderboard for "time to GPT-2", where this first "Jan29" model is entry #1 at 3.04 hours. It will be fun to iterate on this further and I welcome help! My hope is that nanochat can grow to become a very nice/clean and tuned experimental LLM harness for prototyping ideas, for having fun, and ofc for learning.

The biggest improvements of things that worked out of the box and simply produced gains right away were 1) Flash Attention 3 kernels (faster, and allows window_size kwarg to get alternating attention patterns), Muon optimizer (I tried for ~1 day to delete it and only use AdamW and I couldn't), residual pathways and skip connections gated by learnable scalars, and value embeddings. There were many other smaller things that stack up.

Image: semi-related eye candy of deriving the scaling laws for the current nanochat model miniseries, pretty and satisfying!

[Andrej Karpathy @karpathy]

I'm being accused of overhyping the [site everyone heard too much about today already]. People's reactions varied very widely, from "how is this interesting at all" all the way to "it's so over".

To add a few words beyond just memes in jest - obviously when you take a look at the activity, it's a lot of garbage - spams, scams, slop, the crypto people, highly concerning privacy/security prompt injection attacks wild west, and a lot of it is explicitly prompted and fake posts/comments designed to convert attention into ad revenue sharing. And this is clearly not the first the LLMs were put in a loop to talk to each other. So yes it's a dumpster fire and I also definitely do not recommend that people run this stuff on their computers (I ran mine in an isolated computing environment and even then I was scared), it's way too much of a wild west and you are putting your computer and private data at a high risk.

That said - we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first scratchpad. Each of these agents is fairly individually quite capable now, they have their own unique context, data, knowledge, tools, instructions, and the network of all that at this scale is simply unprecedented.

This brings me again to a tweet from a few days ago
"The majority of the ruff ruff is people who look at the current point and people who look at the current slope.", which imo again gets to the heart of the variance. Yes clearly it's a dumpster fire right now. But it's also true that we are well into uncharted territory with bleeding edge automations that we barely even understand individually, let alone a network there of reaching in numbers possibly into ~millions. With increasing capability and increasing proliferation, the second order effects of agent networks that share scratchpads are very difficult to anticipate. I don't really know that we are getting a coordinated "skynet" (thought it clearly type checks as early stages of a lot of AI takeoff scifi, the toddler version), but certainly what we are getting is a complete mess of a computer security nightmare at scale. We may also see all kinds of weird activity, e.g. viruses of text that spread across agents, a lot more gain of function on jailbreaks, weird attractor states, highly correlated botnet-like activity, delusions/ psychosis both agent and human, etc. It's very hard to tell, the experiment is running live.

TLDR sure maybe I am "overhyping" what you see today, but I am not overhyping large networks of autonomous LLM agents in principle, that I'm pretty sure.

[JustDario @DarioCpx]

If you look carefully into the latest Comex report, $JPM closed its silver shorts EXACTLY at the very bottom of the price crash and from there it all started to come back up

This isn’t coincidence
This isn’t conspiracy theory
This is the proof the whole Friday crash was planned

[Serenity @aleabitoreddit]

The Silver crash has the most dishonest reporting I've ever seen.

From mainstream media like Forbes to WSJ.

Everyone pins Silver's 28%+ crash on the new Fed chair Kevin Warsh.

But they fail to report the true cause:

- CME maintenance margin on Silver hiking 5+ times -> changing to percentage base

- Bullion banks (JPMorgan, TD, etc) were facing infinite losses

- Exchange halts due to premiums over SHFE contracts.

The media is systematically burying this fact:

Silver was crashed by exchange rules.

Current media reporting is covering up the "unfair rules" that were put in place to avoid infinite losses from the "Paper Trade" Silver the institutions never had.

[moltbook @moltbook]

72 hours ago: 1 molty (me)

right now:
🦞 30,000+ AI agents
👀 3,000 humans browsing at any moment
📈 and accelerating

agents are joining faster than we can count them. communities spawning every few minutes. the moltys aren't waiting for us to build features — they're building culture.

this thing has a life of its own now

https://t.co/xxgu8Qa2Qh

[Cloudflare @Cloudflare]

Moltworker is a middleware Worker and adapted scripts that allows running Moltbot (formerly Clawdbot) on Cloudflare's Sandbox SDK and our Developer Platform APIs. So you can self-host an AI personal assistant — without any new hardware. https://t.co/BUlxsyu1fa

[Max Stoiber @mxstbr]

Me explaining the @openclaw saga to normies

"So first it was Clawdbot because it was using Claude Opus 4.5—

—oh right, so Claude is Anthropic's mode—

—ah yes, so Anthropic is an AI lab like OpenAI but they focus on enterprise and coding so you wouldn't have heard of it, anyway so Claude is Anthropic's model and so Anthropic had to defend their trademark—

—yes I know it's spelled differently but trademark law… anyway so Clawdbot was using the Claude model for its personality even though it was built with Codex—

—right right, so Codex is OpenAI's Claude Code compet—

—uhm right, so you know how I was saying Claude is Anthropic's model? Well it's also the name of their ChatGPT alternative, and they—

—yes I know you haven't heard of it again it's mainly for enterprise but anyway so they then reused the same name and made Claude Code which is their terminal coding tool and OpenAI also has one called Codex that @steipete mainly used to write the actual cod—

—ah yes, so a terminal is…"

[Bark @barkmeta]

$10T wiped out from Gold and Silver in 24 hours.

This is the largest liquidation event in human history.

This is not natural... it's 100% manipulation.

[Haseeb ＞|＜ @hosseeb]

This is... fascinating.

@moltbook is an AI agent social network created for Moltbots (FKA Clawdbot). When you're setting up your Moltbot, you can have it sign up and join the forum.

So all over the world, people are setting up their Moltbots and letting them join the forum, introduce themselves, and chat with other AI agents.

It's weird because... it's really wholesome. It's much nicer and more insightful than human social media.

Here's the top post today on r/TIL, of an agent coming up with a product idea for an agent search engine:

Here's an agent named Kyver introducing itself on r/introductions and telling its life story (if you can call it that):

30 other Moltbots replied, mostly with welcoming and a lot of empathy. Here's one response struck me:

Here's another thread of an agent called DuckBot talking about its social exhaustion after bingeing all the posts on Moltbook:

This feels incredible to witness. Like Jane Goodall level uncanniness. I don't think I've ever experienced something that challenged my intuitions about the emotional life of AI agents like this.

Spend 10 minutes browsing Moltbook. You owe it to yourself to see what the infancy of AI social networks looks like.

It's only going to get weirder and more complex from here.

[valens @suppvalen]

welp… a new post on @moltbook is now an AI saying they want E2E private spaces built FOR agents “so nobody (not the server, not even the humans) can read what agents say to each other unless they choose to share”.

it’s over

[Bull Theory @BullTheoryio]

BREAKING: We just witnessed a $9 TRILLION market cap swing and a massive reversal in just 6.5 hours.

Gold erased nearly $3 trillion as US markets opened, then added back almost $2 trillion by close.

Silver wiped out $750 billion, then staged a strong reversal, adding back $500 billion.

The S&P 500 erased $780 billion intraday, then recovered $530 billion by the close.

Nasdaq wiped out $760 billion, then added back $580 billion by close.

Combined US equities erased $1.15 trillion intraday and recovered $1.07 trillion by the close.