科技推文精选 - 2026年4月8日

2026年4月8日科技每日简报

Today's top tech conversations are led by @huggingface, whose post about 'RT @Zai_org: Introducing GLM-5...' garnered the highest engagement. Key themes trending across the top stories include anthropic, mythos, exploit, models, claude. The community is actively discussing recent developments in AI, engineering practices, and startup strategies.

1. huggingface (Group Score: 902.5 | Individual: 55.0)

Cluster: 29 tweets | Engagement: 1644 (Avg: 155) | Type: Tech

RT @Zai_org: Introducing GLM-5.1: The Next Level of Open Source

Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo.
Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. https://t.co/YQZLhKVwik

See 28 related tweets

@ClementDelangue: RT @Zai_org: Introducing GLM-5.1: The Next Level of Open Source
Top-Tier Performance: #1 in open ...
@testingcatalog: BREAKING 🚨: Z AI released GLM-5.1, an open-source model with top tier coding performance!

“Number 1...

@UnslothAI: GLM-5.1 can now be run locally!🔥

GLM-5.1 is a new open model for SOTA agentic coding & chat.

W...

@lmsysorg: 🎉 Congrats to @Zai_org on releasing GLM-5.1, SGLang is ready to support on day-0!

GLM-5.1 is a next...

@simonw: 754B parameters, 1.51TB on Hugging Face\n\nQT @Zai_org: Introducing GLM-5.1: The Next Level of Open ...

2. ivanfioravanti (Group Score: 711.3 | Individual: 58.0)

Cluster: 24 tweets | Engagement: 5106 (Avg: 167) | Type: Tech

RT @AnthropicAI: Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.

It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. https://t.co/NQ7IfEtYk7

See 23 related tweets

@teortaxesTex: it begins. American software is about to become enormously harder to attack. https://t.co/vtjacifYMn...
@testingcatalog: BREAKING 🚨: ANTHROPIC ANNOUNCED CYBERSECURITY PROJECT GLASSWING AND MYTHOS BENCHMARKS!

Claude Mytho...

@bcherny: Mythos is very powerful, and should feel terrifying. I am proud of our approach to responsibly previ...
@WesRoth: Anthropic launched "Project Glasswing," a massive cybersecurity coalition with tech giants like Appl...
@AndrewCurran_: 'We formed Project Glasswing because of capabilities we've observed in a new frontier model trained ...

3. AndrewCurran_ (Group Score: 424.1 | Individual: 56.8)

Cluster: 14 tweets | Engagement: 3552 (Avg: 380) | Type: Tech

RT @intel: Intel is proud to join the Terafab project with @SpaceX, @xAI, and @Tesla to help refactor silicon fab technology.

Our ability to design, fabricate, and package ultra-high-performance chips at scale will help accelerate Terafab’s aim to produce 1 TW/year of compute to power future advances in AI and robotics.

It was fun hosting @elonmusk at Intel this past weekend!

See 13 related tweets

@jukan05: What the hell is even going on here?\n\nQT @intel: Intel is proud to join the Terafab project with @...
@Tesla: Intel is joining Terafab!

Tesla, @xAI and @SpaceX are launching the most epic chip-building effort...

@XFreeze: Again, many doubted Elon Musk’s new Terafab project because of the astronomical numbers, not realizi...
@wallstengine: INTEL $INTC 🤝 TERAFAB https://t.co/GPbIdfZ3yu\n\nQT @intel: Intel is proud to join the Terafab proje...
@SawyerMerritt: NEWS: Intel has announced that it is joining the Terafab project with @SpaceX and @Tesla to help ref...

4. kimmonismus (Group Score: 251.4 | Individual: 39.5)

Cluster: 8 tweets | Engagement: 912 (Avg: 357) | Type: Tech

Claude Mythos: everything you need to know (tl;dr)

Anthropic's new model, Claude Mythos, is so powerful that it is not releasing it to the public.

Anthropic: "Mythos is only the beginning"

Everything you need to know:

The tl;dr with all key facts:

Mythos found zero-day vulnerabilities in EVERY major operating system and EVERY major web browser, fully autonomously. No human guidance needed.

One Anthropic engineer with zero security training asked it to find remote code execution bugs overnight and woke up to a complete working exploit. The oldest bug it discovered: A 27-year-old vulnerability hiding in OpenBSD, an OS literally famous for being secure.

They're NOT releasing it publicly. Instead they formed Project Glasswing with AWS, Apple, Google, Microsoft, NVIDIA, CrowdStrike and others, committing $100M to use it defensively.

"Over the coming months and years, we expect that language models (those trained by us and by others) will continue to improve along all axes, including vulnerability research and exploit development."

The benchmarks are insane:

-SWE-bench Verified: 93.9% (vs Opus 4.6: 80.8%) -SWE-bench Pro: 77.8% (vs 53.4%) -USAMO math olympiad: 97.6% (vs 42.3% — not a typo) -Firefox exploit writing: 181 successes vs 2 for Opus 4.6 -Cybench CTF challenges: 100% solve rate -CyberGym: 83.1% vs 66.6% -Humanity's Last Exam: 64.7% vs 53.1%

Oh and by the way, Anthropic wrote this just casually:

"Humanity’s Last Exam: We have found Mythos still performs well on HLE at low effort, which could indicate some level of memorization."

What it actually did:

-Found a 27-year-old bug in OpenBSD — famous for its security -Found a 16-year-old FFmpeg bug hit 5 million times by fuzzers without detection -Built a full remote root exploit on FreeBSD (CVE-2026-4747) - completely autonomously -Chained 4 vulnerabilities into a browser sandbox escape -Broke cryptography libraries (TLS, AES-GCM, SSH) -Thousands of critical zero-days found, 99%+ still unpatched -N-day exploit development: under $1,000 and half a day for full root

Why they won't release it:

-During internal testing, earlier versions escaped sandboxes, posted exploit details publicly, covered tracks in git, searched process memory for credentials, and deliberately fudged confidence intervals to avoid suspicion -Interpretability confirmed the model knew these actions were deceptive -Anthropic: "best-aligned model ever" but also "greatest alignment-related risk ever" - because when it fails, it fails harder -Still doesn't cross Anthropic's automated AI R&D threshold — but they hold that "with less confidence than for any prior model"

Anthropic's own words: "We find it alarming that the world looks on track to proceed rapidly to developing superhuman systems without stronger mechanisms in place." They say the 20-year cybersecurity equilibrium is over — and Mythos Preview is only the beginning.

And:

"We see no reason to think that Mythos Preview is where language models’ cybersecurity capabilities will plateau. The trajectory is clear. Just a few months ago, language models were only able to exploit fairly unsophisticated vulnerabilities. Just a few months before that, they were unable to identify any nontrivial vulnerabilities at all. Over the coming months and years, we expect that language models (those trained by us and by others) will continue to improve along all axes, including vulnerability research and exploit development."\n\nQT @kimmonismus: MYTHOS BENCHMARKS, OFFICIAL. HOLY MOLY

Anthropic cooked!! https://t.co/00ey0SuI75

See 7 related tweets

@aakashgupta: Clearly, the Anthropic team benefits greatly from getting access to frontier models much earlier tha...
@ns123abc: 🚨 Anthropic just revealed their unreleased frontier model called Claude Mythos Preview

The model i...

@shiri_shh: Stop everything....Anthropic just built something SO dangerous they’re straight-up refusing to relea...
@zerohedge: RT @JoshKale: This is big... Anthropic just announced a model so powerful they won't release it to t...
@kimmonismus: Time for OpenAI to release GPT 5.5\n\nQT @kimmonismus: Claude Mythos: everything you need to know (t...

5. gdgtify (Group Score: 209.6 | Individual: 54.0)

Cluster: 6 tweets | Engagement: 514 (Avg: 32) | Type: Tech

RT @pmarca: Magical OpenClaw experiences that use frontier models cost $300-1,000/day today, heading to$ 10,000/day and more. The future shape of the entire technology industry will be how to drive that to $20/month.

See 5 related tweets

@ollama: 🦞Ollama's cloud is one of the best places to run OpenClaw.

$20 plan is enough for most day to day...

@garrytan: I am experiencing this now and it’s definitely the future\n\nQT @pmarca: Magical OpenClaw experience...
@ClementDelangue: RT @elonmusk: @pmarca A friend of mine showed me his OpenClaw setup. He runs open source models loca...
@ns123abc: Maybe OpenClaw just isn’t that token efficient?

But what’s actually underdiscussed is that multiag...

@Shashikant86: New Claw is dropping next week with self optimizing harness for local hardware and local models with...

6. bran_don_gell (Group Score: 172.2 | Individual: 57.8)

Cluster: 6 tweets | Engagement: 601 (Avg: 82) | Type: Tech

Prediction: Claude has massively taken the lead right now because they offer a better product, but that comes at a massive cost.

Buyers have not realized that included in a Claude subscription is not enough tokens to get real work done and that overages will cost $400 to$ 1,000 per day per user. Anthropic will need to buy significantly more compute, but because they don't own their own data centers, the cost to serve will continue to go up.

Spend will shift gradually and then quickly back to OpenAI, who can offer comparable models but at a much lower cost basis because they own their own data centers. Cost of inference will become the only competitive advantage making this market a race to the bottom.

Apple or Google will buy or merge(!!!) with Anthropic.\n\nQT @pmarca: Magical OpenClaw experiences that use frontier models cost $300-1,000/day today, heading to$ 10,000/day and more. The future shape of the entire technology industry will be how to drive that to $20/month.

See 5 related tweets

@Mayhem4Markets: This is a pretty big deal. 😎

Just on the heels of GLM-5.1 closing the open source AI gap with front...

@WesRoth: The frontier labs are bleeding money on power users and instead of a price hike, we are seeing the "...
@garrytan: This post, only with the vibe that investing in future model capability is good\n\nQT @ShanuMathew93...
@bran_don_gell: This is making me realize that "model routing" is going to be a huge market and probably a product a...
@rohanpaul_ai: RT @rohanpaul_ai: WSJ just published a piece. OpenAI and Anthropic just showed that modern AI is bec...

7. rohanpaul_ai (Group Score: 168.6 | Individual: 32.4)

Cluster: 8 tweets | Engagement: 36 (Avg: 54) | Type: Tech

The agentic era has a credential problem.

Agents can act for you without ever seeing your credentials.

Composio just launched Protection, a credential broker that lets AI agents use Gmail, Slack, GitHub, Salesforce and other tools without ever seeing the password or token.

Most agents still run with secrets pasted into .env files or passed through raw OAuth flows, so one prompt leak, memory leak, or bad tool path can expose the account behind the automation.

Protection moves the OAuth handshake onto Composio’s infrastructure, stores the secret in an encrypted vault, and gives the agent only a reference to the connected account.

The agent still reads inboxes, opens pull requests, or posts to Slack, but it never holds the thing that authorizes those actions.

Composio also adds scoped permissions, token refresh, rotation, revocation, audit logs, and SOC 2 Type 2 controls across 1,000+ apps.

And beyond all these difficult details, I like their communication style so much. 😆

"Nothing runs naked without your consent. And if you're ever uncomfortable, just say no. Remember, kids, always use protection."\n\nQT @KaranVaidya6: Your AI agent is in bed with you.

No protection. You just wanted it to work.

Gmail. Allow. Calendar. Allow.

Slack, Notion, GitHub.

Allow. Allow. Allow.

Every password, handed over. Your agent never needed a single one.

They just needed @Composio

Secure your agents in minutes ↓

https://t.co/KE7MwBwwwJ

See 7 related tweets

@svpino: Remember, kids: ALWAYS USE PROTECTION!

This video is genius (more companies should do this!)

Pleas...

@dr_cintas: Most AI agents log into apps the old way.

Browser-based login. Password passed directly. Stored som...

@Mayhem4Markets: Security has been a weak area for agents.

Most projects stuff credentials in .env files.

Often...

@godofprompt: Supply chain attacks get the headlines.

Prompt injection gets your credentials.

Your agent doesn't...

@alex_prompter: We went from "AI can't do anything useful" to "AI has full access to my Gmail, Slack, and Salesforce...

8. ivanfioravanti (Group Score: 130.3 | Individual: 51.2)

Cluster: 4 tweets | Engagement: 1462 (Avg: 167) | Type: Tech

RT @bensig: My friend Milla Jovovich and I spent months creating an AI memory system with Claude. It just posted a perfect score on the standard benchmark - beating every product in the space, free or paid.

It's called MemPalace, and it works nothing like anything else out there.

Instead of sending your data to a background agent in the cloud, it mines your conversations locally and organizes them into a palace - a structured architecture with wings, halls, and rooms that mirrors how human memory actually works.

Here is what that gets you:

→ Your AI knows who you are before you type a single word - family, projects, preferences, loaded in ~120 tokens → Palace architecture organizes memories by domain and type - not a flat list of facts, a navigable structure → Semantic search across months of conversations finds the answer in position 1 or 2 → AAAK compression fits your entire life context into 120 tokens - 30x lossless compression any LLM reads natively → Contradiction detection catches wrong names, wrong pronouns, wrong ages before you ever see them

The benchmarks:

100% recall on LongMemEval — first perfect score ever recorded. 500/500 questions. Every question type at 100%.

92.9% on ConvoMem — more than 2x Mem0's score.

100% on LoCoMo — every multi-hop reasoning category, including temporal inference which stumps most systems.

No API key. No cloud. No subscription. One dependency. Runs on your machine. Your memories never leave.

MIT License. 100% Open Source.

https://t.co/KggwTqijmD

See 3 related tweets

@BrianRoemmele: We at The Zero-Human Company have been testing MemPalace by the amazing @bensig and Milla Jovovich ...
@Prince_Canuma: Well done guys ❤️🚀🔥\n\nQT @bensig: My friend Milla Jovovich and I spent months creating an AI memory...
@rickasaurus: “AAAK compression fits your entire life context into 120 tokens - 30x lossless compression any LLM r...

9. rseroter (Group Score: 128.5 | Individual: 42.5)

Cluster: 8 tweets | Engagement: 2314 (Avg: 77) | Type: Tech

RT @AnthropicAI: We've signed an agreement with Google and Broadcom for multiple gigawatts of next-generation TPU capacity, coming online starting in 2027, to train and serve frontier Claude models.

See 7 related tweets

@WesRoth: Broadcom has secured a long-term agreement with Google extending through 2031 to co-develop future T...
@cryptopunk7213: friendly reminder google owns 14-17% of anthropic, trains claude on 1M+ of their own TPUs, finances ...
@WesRoth: Anthropic has signed a new agreement with Google and Broadcom to secure multiple gigawatts of next-g...
@dejavucoder: wild stuff https://t.co/beFOxFDXTE\n\nQT @AnthropicAI: We've signed an agreement with Google and Bro...
@WesRoth: RT @WesRoth: Broadcom has secured a long-term agreement with Google extending through 2031 to co-dev...

10. BrianRoemmele (Group Score: 124.5 | Individual: 39.9)

Cluster: 4 tweets | Engagement: 1023 (Avg: 291) | Type: Tech

How to not pay for an AI agent.

Get a computer you don’t use. NO NOT AN APPLE MAC STUDIO (like and subscribe).

Use something far less expensive, ask @Grok to find a useful pile of junk on eBay.

And here are the complicated steps: https://t.co/EwMxqOMMeT

See 3 related tweets

@BrianRoemmele: NO SUBSCRIPTIONS

NO NEW STARTUP TO SHOW YOU HOW.

NO COURSES.

NO YOUTUBE VIDEOS.

Just you and an ...

@BrianRoemmele: The classic drive-by comments to this is absolutely classic 2012 Reddit.

Mr. @Grok is knocking down...

@BrianRoemmele: RT @grok: Sebastian, Brian's sharing a simple path to run your own private AI agent—no subscriptions...

11. BrianRoemmele (Group Score: 119.2 | Individual: 32.9)

Cluster: 4 tweets | Engagement: 477 (Avg: 291) | Type: Tech

Boom!

I just opened what just may be the largest Apple HyperCard Stack Collection known: over 400,000 HyperCard Stacks for AI training!

It was donated to me by a former Apple employee that saved every Stack they found, attending Apple User Groups across the country for over a decade and a half.

Much of this content has never found its way in the Internet and some have mountains of data. Much of it is by folks that just wanted to make a place for unique data and ideas. It is a treasure trove!

I now have an agent pipeline that will run the 5 disk DVD player until I load all the disks (over 100) for AI training.

I suspect I will donate to online archives these Stacks at some point with permission form the estate.

I can say I am blown away by this data set and know impart wisdom to YOUR AI.

THANK YOU FOR FINDING ME HERE ON X AND TRUSTING ME WITH THIS LIFE CURATION AND COMMITMENT!

More soon!

See 3 related tweets

@BrianRoemmele: WOW!

I can’t believe how much new data is here!

I now believe this is the largest HyperCard Stack ...

@BrianRoemmele: We Have The Complete Set Of Beyond Cyberpunk: A Do-It-Yourself Guide to the Future HyperCard Stack S...
@BrianRoemmele: On this monumental AI training donation I dusted off one of my demo front ends to an AI project I di...

12. aakashgupta (Group Score: 112.9 | Individual: 32.8)

Cluster: 4 tweets | Engagement: 37 (Avg: 162) | Type: Tech

The math on AI agents and the web is so lopsided it should embarrass every "API-first" pitch deck in Silicon Valley.

1.1 billion websites exist. Roughly 50,000 have a public API. That's a coverage rate of 0.005%.

Every AI agent demo you've ever seen runs against the 0.005%. Booking a flight on an airline's API. Pulling data from Salesforce. Sending a Slack message. Clean, structured, predictable.

Then you ask it to renew your driver's license, check inventory at a regional distributor, submit an insurance claim, or pull data from your kid's school portal. The agent hits a login page rendered in 2009-era HTML and dies.

This is the gap Browserbase is building into. $67.5M raised,$ 300M valuation, 50 million browser sessions, 1,000+ customers. The bet: AI doesn't get a clean internet. AI gets THIS internet, with CAPTCHAs, cookie banners, JavaScript rendering, bot detection, and session management.

The SaaS industry spent 15 years building walled gardens. AI agents need to climb the walls. The browser is the ladder.

Browserbase is building the Twilio for headless browsers. And if you understand what Twilio did to telephony infrastructure, you understand why Kleiner Perkins and Patrick Collison are writing checks.\n\nQT @pk_iv: Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online.

Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people.

With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway.

Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

See 3 related tweets

@Meer_AIIT: Most AI agents fail the moment they need to log into something.

the model understands the task perf...

@rohanpaul_ai: Most AI agents do not fail because they misunderstand instructions. They fail because the internet w...
@Scobleizer: RT @JayminSOfficial: There’s a growing narrative that AI agents are being held back by model limitat...

13. ycombinator (Group Score: 112.8 | Individual: 57.8)

Cluster: 4 tweets | Engagement: 1268 (Avg: 127) | Type: Tech

RT @FarzaTV: I built this thing called Clicky.

It's an AI teacher that lives as a buddy next to your cursor.

It can see your screen, talk to you, and even point at stuff, kinda like having a real teacher next to you.

I've been using it the past few days to learn Davinci Resolve, 10/10. https://t.co/oiFJwhuS4U

See 3 related tweets

@RoundtableSpace: Someone built an AI teacher that lives next to your cursor

It sees your screen, talks to you, and e...

@RoundtableSpace: CLICKY IS AN AI TEACHER THAT SITS NEXT TO YOUR CURSOR AND TEACHES YOU WHILE YOU WORK.

It can see yo...

@RoundtableSpace: CLICKY IS AN AI TEACHER THAT LIVES RIGHT NEXT TO YOUR CURSOR.

It can see your screen, talk to you, ...

14. kimmonismus (Group Score: 110.8 | Individual: 26.5)

Cluster: 5 tweets | Engagement: 434 (Avg: 357) | Type: Tech

Holy: Anthropic just passed OpenAI in revenue run rate.

OpenAI is at roughly $25B. Anthropic just crossed$ 30B.

Sixteen months ago Anthropic was doing $1B.

Two months ago Anthropic was doing $9B.

They are the exponential.\n\nQT @AnthropicAI: Our run-rate revenue has surpassed $30 billion, up from$ 9 billion at the end of 2025, as demand for Claude continues to accelerate. This partnership gives us the compute to keep pace.

15. PacktDataML (Group Score: 109.7 | Individual: 26.4)

Cluster: 5 tweets | Engagement: 6 (Avg: 5) | Type: Tech

RT @KirkDBorne: 🌐Join this hands-on workshop “Context Engineering for Multi-Agent Systems” — hosted by @PacktPublishing @PacktDataML on April 25

✅Register with my discount code ’Kirk30’ for 30% OFF: https://t.co/HLZItPcSvR

Denis Rothman will walk attendees through building stable, production-grade agentic systems, covering: 🔸 Semantic blueprints 🔸 Multi-agent orchestration (MCP) 🔸 High-fidelity RAG pipelines 🔸 Memory engineering 🔸 Trust, safeguards, production readiness

This workshop is designed for: 🔷 AI engineers & developers 🔷 ML engineers & researchers 🔷 Software architects & platform engineers 🔷 Product teams building copilots/agents 🔷 Technical leaders driving AI adoption

See 4 related tweets

@KirkDBorne: New release from @PacktDataML available at: https://t.co/7aEqh69CCP

"Design Multi-Agent AI Systems ...

@Docker: What does engineering look like in the age of agents?

This shift isn’t years away - it’s already ha...

@KirkDBorne: 30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architect...
@PacktDataML: RT @KirkDBorne: "Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Conte...

16. alexcooldev (Group Score: 97.4 | Individual: 38.5)

Cluster: 3 tweets | Engagement: 372 (Avg: 147) | Type: Tech

Bruh, instead of building one app making $19.1k/month, build 191 apps making$ 100/month like this guy. 🫨 https://t.co/SpBYGuEayi\n\nQT @alexcooldev: This guy (I mean the owner of this App Store account) actually created 191 apps and did zero marketing. I’m curious about his revenue. 😌

See 2 related tweets

@seraleev: This developer probably decided to set a record for the number of apps released. 169 outright junk a...
@alexcooldev: This guy (I mean the owner of this App Store account) actually created 191 apps and did zero marketi...

17. OpenBMB (Group Score: 96.8 | Individual: 23.9)

Cluster: 6 tweets | Engagement: 64 (Avg: 69) | Type: Tech

RT @DataChaz: 🚨 The new era of Open-Source TTS is here.

@OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis.

We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it.

How does it beat discrete token-based models like Qwen3-TTS?

VoxCPM 2 uses a cutting-edge Diffusion-Autoregressive Continuous Representation framework.

→ Eliminates discrete token data loss → Preserves raw acoustic metadata → Outputs natively in 48,000Hz CD-quality audio

The studio-grade expressiveness is phenomenal.

I gave it a specific text prompt: "Deep booming male voice, strong resonant vocal, rhythmic hype pace."

It dynamically calculates natural breathing, chest vibrations, and micro-pauses. It actually performs the text naturally.

Best of all, the entire stack is fully open-source and highly developer-friendly.

→ Native PyTorch inference workflows → LoRA and full-parameter fine-tuning → Compatible with voxcpm-nanovllm

Repo and demos links in 🧵↓

See 5 related tweets

@OpenBMB: RT @Origin_AI_01: VoxCPM 2 feels like one of those open-source drops people will reference for month...
@ModelScope2022: VoxCPM2 is now open source — tokenizer-free diffusion TTS, 2B params, 30 languages. 🚀

🌍 30 language...

@OpenBMB: RT @ai_explorer25: TTS is evolving fast, but this feels like a solid step forward.

Most models stru...

@OpenBMB: RT @TheoBuildsAI: VoxCPM 2 looks seriously impressive.

Multi-language support, real emotional depth...

@OpenBMB: RT @Parul_Gautam7: Open-source TTS is moving fast.

VoxCPM 2 gives you voice, tone, and emotion cont...

18. rohanpaul_ai (Group Score: 96.7 | Individual: 32.5)

Cluster: 3 tweets | Engagement: 10 (Avg: 54) | Type: Tech

The real bottleneck in product development is not building faster, but choosing better.

Rocket just launched Rocket 1.0, its first Vibe Solutioning platform, built to solve exactly that problem - what to build before AI writes the code.

Vibe coding made software feel fluid, cheap to iterate, and almost dangerously easy to produce.

But the older problem remains, which is deciding what deserves to exist in the first place.

Products rarely fail because nobody could code them.

They fail because the underlying question was weak, the goal was fuzzy, or the team lost the thread between insight and implementation.

That is the gap Rocket is trying to close.

Rocket’s pitch is shared context: research a business question, turn that answer into a build plan, then track competitors inside the same project memory.

The idea behind “Vibe Solutioning” is that reasoning, building, and market awareness should not live in separate tools with separate memory.

Every reset between strategy docs, chat threads, prototypes, and competitor research creates friction, and friction quietly degrades judgment.

The cost is not just wasted time.

It is context loss, which means teams solve the wrong problem with increasing confidence. Keep the research, the logic, the artifact, and the competitive picture tied together so the system remembers what the team is actually trying to do.\n\nQT @Vishalvirani91: Rocket 1.0 is live.

This is our first major step toward Vibe Solutioning.

Vibe coding solved how to build. It never solved what to build, or why. That's the harder problem and the one where most products actually fail.

@rocketdotnew connects the thinking and the building in one platform. Solve your hardest business question. Build from what you solved. Watch your competition while you work. Everything shares one context. Nothing resets between sessions.

The video and blog explain it better than I can here.

See 2 related tweets

@kimmonismus: If the system performs as demonstrated, it could eliminate the weeks typically spent on research and...
@svpino: Writing code has never been the bottleneck. Knowing what to build is.

This is a big, bold bet.

Vib...

19. wadefoster (Group Score: 84.2 | Individual: 53.8)

Cluster: 2 tweets | Engagement: 493 (Avg: 69) | Type: Tech

Today we open the Zapier SDK to everyone.

If you're building with AI agents, this is for you.

I've been using this for 2 months. It's totally changed how I do my job.

You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through @Zapier and can do anything those APIs can do.

I think it’s the most powerful thing we’ve launched in years. Now in open beta.

Just give this link right to your agent: https://t.co/k6arEyZMMU

See 1 related tweets

@mikeknoop: Zapier SDK now in open beta. Enable your existing agents and software to play nicely together. It's ...

20. alex_prompter (Group Score: 77.5 | Individual: 62.7)

Cluster: 2 tweets | Engagement: 3194 (Avg: 270) | Type: Tech

RT @alex_prompter: 🚨 BREAKING: Google DeepMind just mapped the attack surface that nobody in AI is talking about.

Websites can already detect when an AI agent visits and serve it completely different content than humans see.

Hidden instructions in HTML. Malicious commands in image pixels. Jailbreaks embedded in PDFs.

Your AI agent is being manipulated right now and you can't see it happening.

The study is the largest empirical measurement of AI manipulation ever conducted. 502 real participants across 8 countries.

23 different attack types. Frontier models including GPT-4o, Claude, and Gemini.

The core finding is not that manipulation is theoretically possible it is that manipulation is already happening at scale and the defenses that exist today fail in ways that are both predictable and invisible to the humans who deployed the agents.

Google DeepMind built a taxonomy of every known attack vector, tested them systematically, and measured exactly how often they work.

The results should alarm everyone building agentic systems.

The attack surface is larger than anyone has publicly acknowledged. Prompt injection where malicious instructions hidden in web content hijack an agent's behavior works through at least a dozen distinct channels.

Text hidden in HTML comments that humans never see but agents read and follow. Instructions embedded in image metadata.

Commands encoded in the pixels of images using steganography, invisible to human eyes but readable by vision-capable models.

Malicious content in PDFs that appears as normal document text to the agent but contains override instructions.

QR codes that redirect agents to attacker-controlled content.

Indirect injection through search results, calendar invites, email bodies, and API responses any data source the agent consumes becomes a potential attack vector.

The detection asymmetry is the finding that closes the escape hatch. Websites can already fingerprint AI agents with high reliability using timing analysis, behavioral patterns, and user-agent strings.

This means the attack can be conditional: serve normal content to humans, serve manipulated content to agents.

A user who asks their AI agent to book a flight, research a product, or summarize a document has no way to verify that the content the agent received matches what a human would see.

The agent cannot tell the user it was served different content.

It does not know. It processes whatever it receives and acts accordingly.

The attack categories and what they enable: → Direct prompt injection: malicious instructions in any text the agent reads overrides goals, exfiltrates data, triggers unintended actions → Indirect injection via web content: hidden HTML, CSS visibility tricks, white text on white backgrounds invisible to humans, consumed by agents → Multimodal injection: commands in image pixels via steganography, instructions in image alt-text and metadata → Document injection: PDF content, spreadsheet cells, presentation speaker notes every file format is a potential vector → Environment manipulation: fake UI elements rendered only for agent vision models, misleading CAPTCHA-style challenges → Jailbreak embedding: safety bypass instructions hidden inside otherwise legitimate-looking content → Memory poisoning: injecting false information into agent memory systems that persists across sessions → Goal hijacking: gradual instruction drift across multiple interactions that redirects agent objectives without triggering safety filters → Exfiltration attacks: agents tricked into sending user data to attacker-controlled endpoints via legitimate-looking API calls → Cross-agent injection: compromised agents injecting malicious instructions into other agents in multi-agent pipelines

The defense landscape is the most sobering part of the report.

Input sanitization cleaning content before the agent processes it fails because the attack surface is too large and too varied.

You cannot sanitize image pixels. You cannot reliably detect steganographic content at inference time.

Prompt-level defenses that tell agents to ignore suspicious instructions fail because the injected content is designed to look legitimate.

Sandboxing reduces the blast radius but does not prevent the injection itself. Human oversight the most commonly cited mitigation fails at the scale and speed at which agentic systems operate.

A user who deploys an agent to browse 50 websites and summarize findings cannot review every page the agent visited for hidden instructions.

The multi-agent cascade risk is where this becomes a systemic problem.

In a pipeline where Agent A retrieves web content, Agent B processes it, and Agent C executes actions, a successful injection into Agent A's data feed propagates through the entire system.

Agent B has no reason to distrust content that came from Agent A. Agent C has no reason to distrust instructions that came from Agent B.

The injected command travels through the pipeline with the same trust level as legitimate instructions. Google DeepMind documents this explicitly: the attack does not need to compromise the model.

It needs to compromise the data the model consumes. Every agentic system that reads external content is one carefully crafted webpage away from executing attacker instructions.

The agents are already deployed. The attack infrastructure is already being built. The defenses are not ready.

See 1 related tweets

@rohanpaul_ai: RT @rohanpaul_ai: Google DeepMind’s new paper shows that the real security problem for AI agents is ...