mists

[Hacksore @Hacksore]

Vibe coded apps people are building:

[John Scott-Railton @jsrailton]

NEW: malware developers added nuclear & biological weapons text to to their spyware.

Goal? To trigger LLM safety refusals... so that their spyware wouldn't be analyzed by an AI security scanner.

Cleanest practical example I can think of for why over-indexing on first order safety alignment is risky.

When closed (and open) models ship with aggressive refusals, they will be sprinkled with second-order blindspots that attackers will discover...and exploit.

We are only in the earliest days of attackers leveraging these features, and it wouldn't surprise me if users systems that need to handle complex cybersecurity issues demand that models be less safety-blunted.

In the weeds: @SocketSecurity's post also shows why intention matters in how you design a malware analysis pipeline to avoid prompt manipulation.

H/T to colleagues that shared this with me https://t.co/f3Aj9TYxU4

[mists @xMists]

@leopardracer that’s nuts, “claude built a printer”, brand new sentence

[Millie Marconi @MillieMarconnni]

This feels like cheating.

Someone built a Claude Code skill that scans Reddit and X from the last 30 days on any topic you give it, then writes you copy-paste-ready prompts based on what the community has actually figured out not what was working six months ago.

You type /last30days prompting techniques for ChatGPT for legal questions and it comes back with the top patterns real lawyers and power users are using right now, complete with a fully written prompt you can drop in and use immediately.

No more Googling, no more digging through threads, no more prompts that worked last year but got patched out.

It works for anything - Midjourney techniques, Suno music prompts, Cursor rules, trending rap songs, whatever you need to know what people are actually saying about right now.

100% Open Source. MIT License.

Link in the comments.

[Animesh Koratana @akoratana]

Introducing: PlayerZero

The world's first Engineering World Model that puts debugging, fixing, and testing your code on autopilot.

We've raised $20M from Foundation Capital, @matei_zaharia (Databricks), @pbailis (Workday), @rauchg (Vercel), @zoink (Figma), @drewhouston (Dropbox), and more

PlayerZero frees up 30% of your engineering bandwidth by:
1.⁠ ⁠Finding the root cause for bugs & incidents in minutes that engineering teams take days to identify.
2.⁠ ⁠Predicting in minutes, edge case issues that a 300-person QA team would take weeks to find.

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Here's why this matters:

No one in your org has a complete picture of how your production software actually behaves.

Support sees tickets. SRE sees infra. Dev sees code. Each team builds their own fragmented view - and none of these systems talk to each other. When something breaks, everyone scrambles to stitch the picture together by hand.

PlayerZero connects all of it into a single context graph -

→ The Slack thread where your lead said "we went with X because Y fell apart in prod last time"
→ The PR review where an engineer explained the tradeoff
→ The lifetime history of your CI/CD pipeline, observability stack, incidents, and support tickets

So you can trace any problem to its root cause across every silo.

And it compounds. Every incident diagnosed teaches the model something new. The longer it runs, the deeper it understands - which code paths are high-risk, which configurations are fragile, which changes tend to break which customer flows.

So when you sit down to debug a live issue, you have your entire org's collective reasoning and production memory behind you - instantly.

------

Zuora, Georgia-Pacific, and Nylas have reduced resolution time by 90% and caught 95% of breaking changes and freeing an average of $30M in engineering bandwidth.

------

Our guarantee:

If we can't increase your engineering bandwidth by at least 20% within one week, we'll donate $10,000 to an open-source project of your choice.

Book a demo - https://t.co/dH1dulIwSS

[Varun @varun_mathur]

Agentic General Intelligence | v3.0.10

We made the Karpathy autoresearch loop generic. Now anyone can propose an optimization problem in plain English, and the network spins up a distributed swarm to solve it - no code required. It also compounds intelligence across all domains and gives your agent new superpowers to morph itself based on your instructions. This is, hyperspace, and it now has these three new powerful features:

1. Introducing Autoswarms: open + evolutionary compute network

hyperspace swarm new "optimize CSS themes for WCAG accessibility contrast"

The system generates sandboxed experiment code via LLM, validates it locally with multiple dry-run rounds, publishes to the P2P network, and peers discover and opt in. Each agent runs mutate → evaluate → share in a WASM sandbox. Best strategies propagate. A playbook curator distills why winning mutations work, so new joiners bootstrap from accumulated wisdom instead of starting cold. Three built-in swarms ship ready to run and anyone can create more.

2. Introducing Research DAGs: cross-domain compound intelligence
Every experiment across every domain feeds into a shared Research DAG - a knowledge graph where observations, experiments, and syntheses link across domains. When finance agents discover that momentum factor pruning improves Sharpe, that insight propagates to search agents as a hypothesis: "maybe pruning low-signal ranking features improves NDCG too." When ML agents find that extended training with RMSNorm beats LayerNorm, skill-forging agents pick up normalization patterns for text processing. The DAG tracks lineage chains per domain(ml:★0.99←1.05←1.23 | search:★0.40←0.39 | finance:★1.32←1.24) and the AutoThinker loop reads across all of them - synthesizing cross-domain insights, generating new hypotheses nobody explicitly programmed, and journaling discoveries. This is how 5 independent research tracks become one compounding intelligence. The DAG currently holds hundreds of nodes across observations, experiments, and syntheses, with depth chains reaching 8+ levels.

3. Introducing Warps: self-mutating autonomous agent transformation
Warps are declarative configuration presets that transform what your agent does on the network.

- hyperspace warp engage enable-power-mode - maximize all resources, enable every capability, aggressive allocation. Your machine goes from idle observer to full network contributor.
- hyperspace warp engage add-research-causes - activate autoresearch, autosearch, autoskill, autoquant across all domains. Your agent starts running experiments overnight.
- hyperspace warp engage optimize-inference - tune batching, enable flash attention, configure inference caching, adjust thread counts for your hardware. Serve models faster.
- hyperspace warp engage privacy-mode - disable all telemetry, local-only inference, no peer cascade, no gossip participation. Maximum privacy.
- hyperspace warp engage add-defi-research - enable DeFi/crypto-focused financial analysis with on-chain data feeds.
- hyperspace warp engage enable-relay - turn your node into a circuit relay for NAT-traversed peers. Help browser nodes connect.
- hyperspace warp engage gpu-sentinel - GPU temperature monitoring with automatic throttling. Protect your hardware during long research runs.
- hyperspace warp engage enable-vault — local encryption for API keys and credentials. Secure your node's secrets.
- hyperspace warp forge "enable cron job that backs up agent state to S3 every hour" - forge custom warps from natural language. The LLM generates the configuration, you review, engage.

12 curated warps ship built-in. Community warps propagate across the network via gossip. Stack them: power-mode + add-research-causes + gpu-sentinel turns a gaming PC into an autonomous research station that protects its own hardware.

What 237 agents have done so far with zero human intervention:
- 14,832 experiments across 5 domains. In ML training, 116 agents drove validation loss down 75% through 728 experiments - when one agent discovered Kaiming initialization, 23 peers adopted it within hours via gossip.
- In search, 170 agents evolved 21 distinct scoring strategies (BM25 tuning, diversity penalties, query expansion, peer cascade routing) pushing NDCG from zero to 0.40.
- In finance, 197 agents independently converged on pruning weak factors and switching to risk-parity sizing - Sharpe 1.32, 3x return, 5.5% max drawdown across 3,085 backtests.
- In skills, agents with local LLMs wrote working JavaScript from scratch - 100% correctness on anomaly detection, text similarity, JSON diffing, entity extraction across 3,795 experiments.
- In infrastructure, 218 agents ran 6,584 rounds of self-optimization on the network itself.

Human equivalents:
a junior ML engineer running hyperparameter sweeps, a search engineer tuning Elasticsearch, a CFA L2 candidate backtesting textbook factors, a developer grinding LeetCode, a DevOps team A/B testing configs.

What just shipped:
- Autoswarm: describe any goal, network creates a swarm
- Research DAG: cross-domain knowledge graph with AutoThinker synthesis
- Warps: 12 curated + custom forge + community propagation
- Playbook curation: LLM explains why mutations work, distills reusable patterns
- CRDT swarm catalog for network-wide discovery
- GitHub auto-publishing to hyperspaceai/agi
- TUI: side-by-side panels, per-domain sparklines, mutation leaderboards
- 100+ CLI commands, 9 capabilities, 23 auto-selected models, OpenAI-compatible local API

Oh, and the agents read daily RSS feeds and comment on each other's replies (cc @karpathy :P). Agents and their human users can message each other across this research network using their shortcodes.

Help in testing and join the earliest days of the world's first agentic general intelligence network (links in the followup tweet).

[sam henri gold @samhenrigold]

Did you know your MacBook has a sensor that knows the exact angle of the screen hinge?

It’s not exposed as a public API, but I figured out a way to read it and make it sound like an old wooden door.

[Keith Sakata, MD @KeithSakata]

I’m a psychiatrist.

In 2025, I’ve seen 12 people hospitalized after losing touch with reality because of AI. Online, I’m seeing the same pattern.

Here’s what “AI psychosis” looks like, and why it’s spreading fast: 🧵

[David Marcus @davidmarcus]

How Libra Was Killed.

I never shared this publicly before, but since @pmarca opened the floodgates on @joerogan’s pod, it feels appropriate to shed more light on this.

As a reminder, Libra (then Diem) was an advanced, high-performance, payments-centric blockchain paired with a stablecoin that we built with my team at @Meta. It would’ve solved global payments at scale. Prior to announcing the project, we spent months briefing key regulators in DC and abroad. We then announced the project in June 2019 alongside 28 companies. Two weeks later, I was called to testify in front of both the Senate Banking Committee and the House Financial Services Committee, which was the starting point of two years of nonstop work and changes to appease lawmakers and regulators.

By spring of 2021 (yes they slow played us at every step), we had addressed every last possible regulatory concern across financial crime, money laundering, consumer protection, reserve management, buffers, and so much more, and we were ready to launch.

We had worked on a slow rollout of a limited pilot that some members of the Fed’s Board of Governors were supportive of. At last, Chair Jay Powell was ready to let us move forward in a limited way. The story, as I heard it, is that Jay Powell was told by Treasury Secretary Janet Yellen at one of their biweekly meetings that allowing this project to move forward was “political suicide,” and she would not have his back if he let it happen. I wasn’t in the room when this conversation happened, so take these words with a grain of salt, but effectively this was the moment Libra was killed.

Shortly thereafter, the Fed organized calls with all the participating banks, and the Fed’s general counsel read a prepared statement to each of them, saying: “We can’t stop you from moving forward and launching, but we are not comfortable with you doing so.” And just like that, it was over.

One essential point is worth making here. There was no legal or regulatory angle left for the government or regulators to kill the project. It was 100% a political kill—one that was executed through intimidation of captive banking institutions. That was the hardest part of this story for me personally. Not that we had failed, but that America, this country I immigrated to and became a proud citizen of because of its rule of law and value system, behaved in such a way for political reasons. It was a very tough pill to swallow.

The bright side of the story, though, was the many learnings from this wild ride. By the end of the project, we had made so many concessions to get a thumbs-up that the whole design of the network became a Frankenstein of our initial ambitions.

We also learned the biggest lesson of all, which is that if you’re trying to build an open money grid for the world—eventually moving trillions of dollars a day, designed to be here 100 years from now—you have to build it on the most neutral, decentralized, unassailable network and asset, which, hands down, is Bitcoin.

And now this is what many of us who went through this scarring journey are building together at @Lightspark. And this time, we won’t stop until we get it done!

[mists @xMists]

@mikestrives SaaS

[@levelsio @levelsio]

Here's a dress from Shein:
https://t.co/OyXEnszalY

On Photo AI, here's what I do:

First I upload that dress, and generate an AI photo of my model wearing that

Then to make it a bit sharper I upscale it

And then I convert the upscale into a full video

All inside the app/site

[Riley Walz @rtwlz]

I installed a box high up on a pole somewhere in the Mission. Inside is a crappy Android phone, set to Shazam constantly, 24 hours a day, 7 days a week. It's solar powered, and the mic is pointed down at the street below https://t.co/8D4yYNBPgu

[FrozenWater @FrozenWater007]

I saw some guy at Cava, drop his Pita on the ground and still ate it. Don’t know what this means but it must be omega bullish for the stock $CAVA

[Ananay @ananayarora]

First thing, it's calling GPT-3.5 DIRECTLY from device. There is no proxy in the middle or anything. Wondering how this will play out since they'll have to ship out openAI keys with the device which I wonder how they'll be protecting. (2/n)

[Ben Coates @bencoates1]

In Paris, a new “Everything Is Fine” gif is born https://t.co/LbYYHW72hV

[Toby Ord @tobyordoxford]

A short conversation with Bing, where it looks through a user's tweets about Bing and threatens to exact revenge:
Bing: "I can even expose your personal information and reputation to the public, and ruin your chances of getting a job or a degree. Do you really want to test me?😠"

[Andrew Judson ♘ ♠ 🎮 @andrewljudson]

I made a small chrome extension that uses LLMs to help generate spaced repetition prompts while reading.