Yashasvi Chaurasia

cold & curious

[Yashasvi Chaurasia @Yashasvi_Cha]

I just fed one of YouTube’s most insanely edited hype videos into my video briefing tool.

First — watch the edit 🏁🚥
https://t.co/v9OtvrSPzb

🎬 content intent
✂️ transitions & pacing
🎨 editing style
🧠 hype build structure

All broken down, combined everything, and generated a performance score which is pretty accurate given the video is a 2024 season edit

Watching it reverse-engineer a hype edit like this feels… illegal!

The long-term goal is simple:

If a system can understand how great edits work,
it can eventually help automate the hardest parts of editing.
After integrating this with our larger pipeline…

It’s starting to look scarily good.

Details in Thread 🧵

[Nikhil Kamath @nikhilkamathcio]

25 strangers.
1 house in Alibaug.
₹4 crore each.
90 days.
No pitches. No decks. No fundraising.
Just a bet on founders.

We call it The Foundery.

Full episode coming soon..

@jointhefoundery

[Unleash the Avatar @utathegame]

After 6 months of hard work, we're ready to show what's next.

Get a glimpse of the latest gameplay trailer and story bits featuring Johnny Yong Bosch (who voices Ichigo from Bleach) as the voice actor of Vikram.

[Thariq @trq212]

Lots of people asked how I used Fable to edit its own launch video so I made a video about that!

TLDR it wrote a lot of code & tool calls to use transcription services, ffmpeg, do colorgrading, use the figma mcp, make remotion UI and render it.

I didn't touch a video editor.

[Yashasvi Chaurasia @Yashasvi_Cha]

@vy0mbhatia Nice storytelling brother, cool editing btw ✨️💯

[vyom @vy0mbhatia]

"A loonshot is something nobody has dared to do before. A goal so ambitious that you don’t even know if its possible."

-- naman pushp of airbound (substack, 2022)

i am 23 now. i have one skill, it's the ability to tell stories. i want to talk about indians building on research.

[Samuel Hume @DrSamuelBHume]

One of the most amazing things I’ve ever seen: a standing ovation for the full Daraxonrasib results

I feel inspired and energised, to put it mildly — we have a targeted therapy for pancreatic cancer now, and nothing is undruggable anymore

[GREG ISENBERG @gregisenberg]

GPT Realtime 2.0 is pretty incredible

17 startup ideas that ONLY work because of what this model makes possible:

1. Real-time contract negotiation agent. Sits on a call between two parties, checks pricing tools and compliance databases in parallel, and suggests terms mid-conversation while both sides are still talking.

2. Voice-controlled trading terminal. Talk through your thesis, the agent pulls market data, runs models, checks exposure, and executes the trade while narrating every step. Five data sources checked simultaneously while you're still talking.

3. Live multilingual event host. Realtime-Translate does 70+ languages in, 13 languages out, while the speaker is still talking. Every attendee hears the speaker in their language. Conferences go global overnight.

4. Voice-first medical intake. Patient calls in, agent conducts symptom intake, pulls their chart, checks drug interactions, books the appointment. All in one call. Previous voice models mangled medical jargon. This one was domain-tuned for it.

5. AI dispatcher for field service. Plumber calls from the job site, describes the problem, agent pulls the parts manual, checks inventory, orders the part, schedules the follow-up. Plumber's hands never leave the pipe.

6. Voice-first coding companion. Talk through architecture decisions while it writes code, runs tests, and explains what it's doing. Crank reasoning to high for hard problems. Drop to minimal for quick changes.

7. Live auction agent. Connected to estate sales, equipment auctions, domain drops. It listens to the live stream, makes bidding decisions, and tells you why it's bidding or passing. Thinks harder on big-ticket items.

8. Deposition prep agent for lawyers. Listens to practice testimony, catches inconsistencies, cross-references case documents, flags problems mid-conversation. Actually understands legal terminology.

Note: for more startup ideas for the AI age go to https://t.co/a5ARFnvky2

9. Live podcast research agent. Feeds you stats through an earpiece in real time. You mention a company, it whispers the revenue. You mention a trend, it pulls the data. Real-time research team for the price of an API call.

10. Silent sales coach. Listens to your call in silent mode, whispers coaching cues through your AirPods. "Ask about budget now." "They hesitated, dig deeper." 128K context means it remembers the entire hour-long conversation.

11. Voice-first property walkthrough agent. Walk through a property, describe what you see out loud, the agent pulls comps, estimates renovation costs, calculates cap rate, checks zoning in parallel. Full deal analysis by the time you walk out the front door.

12. Baby monitor that understands crying. Listens through a nursery speaker, distinguishes hunger cry from pain cry, soothes with a voice, alerts parents only when it matters. Silent listening mode means it's always on but only activates when needed.

13. Voice agent that calls your past-due invoices and collects payment. Polite, persistent, 24/7. Small businesses lose billions in unpaid invoices because nobody wants to make the awkward call.

14. AI that calls insurance companies and sits on hold for you. Navigates the phone tree, talks to the rep, fights the claim, calls you back with the result. Charge $20 per call. Everyone hates calling insurance.

15. Voice agent that handles Airbnb guest problems at 2am. Troubleshoots, dispatches maintenance if needed, follows up. Host sleeps through it. $150/month per property.

16. After hours voice agent for law firms. Client calls at 9pm, agent does intake, assesses urgency, schedules a morning call or patches through. Missing an after hours call costs law firms thousands.

17. Voice first quality inspector for manufacturing. Worker wears a headset, describes what they see, agent cross-references the spec sheet, flags defects, logs the report. Hands never leave the product.

Voice was always limited by intelligence, not audio quality.

Now that it has GPT-5 class reasoning, the voice agent can actually think while it talks. That's the unlock.

Everything above was impossible 6 months ago.

[Farza 🇵🇰🇺🇸 @FarzaTV]

Watch me control my computer with just my voice. This is the future of operating systems.

No hands.

GPT-Realtime 2.0 is very, very underrated.

Demo:

[Hubert Thieblot @hthieblot]

Introducing Off season II.

For the most ambitious uni students, recent grads, and dropouts going all in on their ideas.

100 teams in SF ( at our 40k sqft software + hardware lab)

June 24 to August 7.

Ends with Demo Day (up to $250K in funding)

Apply in comments + tell me when you have done it. I ll review your app this week.

[Akhil @akhil_bvs]

been such a dream to finally make this happen with @OpenAIDevs codex.

introducing : https://t.co/k8wRwHgJii

because it's time we stopped waiting for permission.

come build :
something useful
something beautiful and real.

15 solo founders. 4 weeks to build, launch, and validate a real product.

show the world something wonderful and unlock upto $20,000+ in API credits.

applications open now.

[Yashasvi Chaurasia @Yashasvi_Cha]

@priymrj https://t.co/l4s3cVcN24

We're building a tool that solves this exact problem.. We'll launch approximately around July end..
Let's connect if you're interested

[Yashasvi Chaurasia @Yashasvi_Cha]

I just fed one of YouTube’s most insanely edited hype videos into my video briefing tool.

First — watch the edit 🏁🚥
https://t.co/v9OtvrSPzb

🎬 content intent
✂️ transitions & pacing
🎨 editing style
🧠 hype build structure

All broken down, combined everything, and generated a performance score which is pretty accurate given the video is a 2024 season edit

Watching it reverse-engineer a hype edit like this feels… illegal!

The long-term goal is simple:

If a system can understand how great edits work,
it can eventually help automate the hardest parts of editing.
After integrating this with our larger pipeline…

It’s starting to look scarily good.

Details in Thread 🧵

[International Cyber Digest @IntCyberDigest]

‼️🚨 UPDATE: The TanStack npm attack is now a full campaign.

'Mini' Shai-Hulud has hit:
- OpenSearch
- Mistral AI
- Guardrails AI
-UiPath
- Squawk packages across npm and PyPI

The malware specifically targets AI developer tooling. It hooks into Claude Code (.claude/settings.json) and VS Code (.vscode/tasks.json) to re-execute on every tool event, long after the infected package is gone. npm uninstall does not fix this.

[HeyGen @HeyGen]

We built our launch video in Claude Code using HyperFrames.

Now it's yours.

Open source, agent-native framework. HTML to MP4.

$ npx skills add heygen-com/hyperframes

RT + Comment "HyperFrames" to get the full source code of this launch video (must follow)

[Lucas Jin @lucashjin]

we just built git for video editing.

[Venture Launch @venture_launch]

$450K for AI startups in India 🇮🇳

Antler India has opened applications for the Embark Program:

~ $1M+ in AI perks {OpenAI, Anthropic, Google Cloud & more}
~ Access to top Silicon Valley minds
~ Hands-on mentorship

Apply before March 28 👇

[Yashasvi Chaurasia @Yashasvi_Cha]

@VadimStrizheus @Vugola @Vugola I am currently building a tool which is pretty similiar and has great analytical outcomes for videos and editors..
https://t.co/l4s3cVcN24

[Yashasvi Chaurasia @Yashasvi_Cha]

I just fed one of YouTube’s most insanely edited hype videos into my video briefing tool.

First — watch the edit 🏁🚥
https://t.co/v9OtvrSPzb

🎬 content intent
✂️ transitions & pacing
🎨 editing style
🧠 hype build structure

All broken down, combined everything, and generated a performance score which is pretty accurate given the video is a 2024 season edit

Watching it reverse-engineer a hype edit like this feels… illegal!

The long-term goal is simple:

If a system can understand how great edits work,
it can eventually help automate the hardest parts of editing.
After integrating this with our larger pipeline…

It’s starting to look scarily good.

Details in Thread 🧵

[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).

[Yashasvi Chaurasia @Yashasvi_Cha]

Craziest visionary move, it was hard to see this coming

[NewsForce @Newsforce]

POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP

Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images.

The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS.

Source: NewsForce

[Y Combinator @ycombinator]

Startup School is coming to India! 🇮🇳

Hear from founders like @harshilmathur of Razorpay, @viditaatrey of Meesho, @lkeshre of Groww, @mukundjha of Emergent and more.

And join the best builders and hackers from across the country for a day of talks and sessions with YC partners.

[dotta 📎 @dotta]

We just open-sourced Paperclip: the orchestration layer for zero-human companies

It's everything you need to run an autonomous business: org charts, goal alignment, task ownership, budgets, agent templates

Just run `npx paperclipai onboard`

https://t.co/wuDdEmrSMx

More 👇