科技推特热门精选 - 2026年4月19日

2026年4月19日科技每日简报

Today's top tech conversations are led by @gregisenberg, whose post about 'I used Claude Design and I kin...' garnered the highest engagement. Key themes trending across the top stories include https, design, claude, software, regulatory. The community is actively discussing recent developments in AI, engineering practices, and startup strategies.

1. gregisenberg (Group Score: 150.2 | Individual: 31.6)

Cluster: 7 tweets | Engagement: 818 (Avg: 912) | Type: Tech

I used Claude Design and I kinda hate how good it is

Have you tried it?\n\nQT @claudeai: Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude.

Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day. https://t.co/2BgBGtgYGX

See 6 related tweets

@RoundtableSpace: https://t.co/hyKRTxNbhx

Figma CEO seeing this Claude update https://t.co/Efvgm86xrU\n\nQT @claudeai...

@chddaniel: Motion designers are cooked...

This AI agent made this video in 25 mins, only did 2 changes. https:...

@chhddavid: Video editors and motion designers are cooked...

Claude made this video in 25 mins, only did 2 chan...

@adonis_singh: first time trying to use claude design, its amazing. https://t.co/wN1KqZ1fOi\n\nQT @theo: Took a bit...
@minchoi: RT @minchoi: It's over for... Adobe and Figma

Anthropic just dropped Claude Design.

Now you can ta...

2. Hesamation (Group Score: 143.6 | Individual: 38.3)

Cluster: 5 tweets | Engagement: 5944 (Avg: 824) | Type: Tech

RT @Hesamation: Google DeepMind researcher argues that LLMs can never be conscious, not in 10 years or 100 years.

"Expecting an algorithmic description to instantiate the quality it maps is like expecting the mathematical formula of gravity to physically exert weight." https://t.co/J7KWRZ48JE

See 4 related tweets

@Hesamation: “Expecting an algorithmic description to instantiate the quality it maps is like expecting the mathe...
@GenAI_is_real: as someone who works on making LLMs run faster and cheaper every day, i can confidently say the ques...
@MLStreetTalk: > 1980: John Searle explains why we can't abstract away the causal properties that actually produce ...
@burkov: Read with an AI tutor: https://t.co/H2OlOQmVsL\n\nQT @Hesamation: Google DeepMind researcher argues ...

3. jukan05 (Group Score: 116.7 | Individual: 46.4)

Cluster: 3 tweets | Engagement: 1899 (Avg: 619) | Type: Tech

Software will never return to the era when it commanded 50x revenue multiples…

Software companies now have to fight not just for growth, but for survival itself.

This is a truly great piece. You should definitely read it.\n\nQT @blyons151: In August I wrote a thesis I never published. The funds I was warning were key Crossover Research clients, so I stayed quiet. Since then, 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲𝘀 𝗮𝗿𝗲 𝗱𝗼𝘄𝗻 𝟱𝟬%+. Salesforce $CRM, ServiceNow$ NOW, Adobe $ADBE, Workday$ WDAY all off 40% from highs. Thomson Reuters $TRI dropped 16% in a single session on the Anthropic legal agent launch. The SaaSpocalypse arrived. So here's the follow-up. Not commentary on what happened, but where I think this goes next.

Most vertical SaaS companies aren't underperforming because their software is bad. 𝗧𝗵𝗲𝘆'𝗿𝗲 𝘂𝗻𝗱𝗲𝗿𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲𝘆 𝗻𝗲𝘃𝗲𝗿 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀. And the first business is under attack. For twenty years, one of the biggest SaaS moats was engineering complexity: deep technical talent, long roadmaps, compounding codebases that were genuinely hard to replicate. 𝗔𝗜 𝘂𝗽𝗲𝗻𝗱𝗲𝗱 𝘁𝗵𝗮𝘁 𝗮𝗹𝗺𝗼𝘀𝘁 𝗼𝘃𝗲𝗿𝗻𝗶𝗴𝗵𝘁.

Product development is democratizing to operators with no code background but strong product vision. Look at Anthropic: they've built the engine and are shipping lookalike products at a cadence that would have taken a legacy SaaS vendor three years of roadmap, with a fraction of the headcount. That pace can kill legacy businesses overnight.

𝗜𝗳 𝘁𝗵𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗺𝗼𝗮𝘁 𝗶𝘀 𝗴𝗼𝗻𝗲, 𝗳𝗼𝘂𝗿 𝗺𝗼𝗮𝘁𝘀 𝗿𝗲𝗺𝗮𝗶𝗻: 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻, 𝗽𝗿𝗼𝗽𝗿𝗶𝗲𝘁𝗮𝗿𝘆 𝗱𝗮𝘁𝗮, 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗯𝗿𝗲𝗮𝗱𝘁𝗵, 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗶𝗻𝘀𝘂𝗹𝗮𝘁𝗶𝗼𝗻. The first three are moats the company builds. The fourth is a moat the company captures, and it's the one most resistant to AI disruption.

𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝘀𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀 𝘁𝗵𝗮𝘁 𝗵𝗮𝘃𝗲 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝘁𝗼 𝗱𝗼 𝘄𝗶𝘁𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝘆. Once a vendor is embedded in a compliance workflow, ripping them out means re-attesting, re-auditing, and re-certifying every downstream process. The buyer isn't paying for software, they're paying for the accumulated paper trail. Tyler Technologies ($TYL) is the clearest version of the pattern. State and local government software across courts, public safety, assessment, and ERP. Every module is married to statutory process, FIPS, CJIS, audit trails, and procurement cycles that take years. TYL is down 42% TTM and 2026 guidance came in soft, but the moat didn't break. Revenue still compounded, and government procurement runs on five-year cycles, not five-week news cycles. Veeva is the sharper version. Revenue up 16% in FY26, Q4 beat, the stock still down 25%. The market is selling execution, not weakness. Guidewire in P&C insurance, where regulatory filings and rate approvals anchor the stack, sits in the same setup: still compounding ARR, still winning cloud conversions, multiple reset anyway. Same pattern across all three: multiples compressed, fundamentals intact. The moat is the regulatory surface area itself, and it compounds because the rules get more complex, not less.

𝗜 𝘄𝗮𝘀 𝗹𝗼𝗻𝗴 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 𝗮𝘁 $𝟭𝟯 (read that here: https://t.co/0N0oIX8N87). 𝗡𝗼𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗼𝗿 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹𝗶𝗻𝗴. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝘆. Palantir is the proprietary-data version of the regulatory thesis. Once Palantir sits between the customer and their own data, ripping it out means rebuilding the data model from scratch. Snowflake and Databricks never had that entrenchment layer. AIP bootcamps then turned the data moat into a distribution moat: 660 bootcamps in a single quarter, 94% y/y US customer deal growth, bookings at 1.9x sales. Own the data, ship functional AI on top of it, let the GTM compound. Every vertical incumbent has a version of this available. The question is whether they'll build it before a challenger does.

But regulatory insulation is necessary, not sufficient. Plenty of vendors inside regulated verticals are still getting squeezed because they never became AI-native. BlackLine ( $BL) and Trintech are feeling it in close and reconciliation as Numeric, Maximor, and Stacks build AI-native from day one. nCino ($ NCNO) in banking faces the same challenge. The regulatory moat buys you time. It doesn't buy you the decade.

𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 𝗳𝗼𝗿𝗺𝘂𝗹𝗮 𝗶𝘀 𝗱𝗮𝘁𝗮 𝗼𝗿 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗮𝗿𝗲𝗮 𝗽𝗹𝘂𝘀 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜, 𝗻𝗼𝘁 𝗼𝗻𝗲 𝗼𝗿 𝘁𝗵𝗲 𝗼𝘁𝗵𝗲𝗿. Look at why Claude is winning. Anthropic isn't competing on model benchmarks, they're competing on functional workflow. Building for the user, not the leaderboard. That's the playbook vertical incumbents need to run. Take the moat you already have, whether it's regulatory or data-entrenchment, layer genuine workflow AI on top, and the challenger can't catch you. The vendors that do both win the decade. The ones that rely on inertia alone get caught. The ones that ship AI without an anchor get commoditized. You need both.

𝗧𝗵𝗲 𝗯𝘂𝘆𝗲𝗿 𝗶𝘀 𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝘆𝗼𝘂 𝘁𝗵𝗶𝘀 𝗽𝗹𝗮𝗶𝗻𝗹𝘆. A study we ran with Battery Ventures on AI adoption in the Office of the CFO (https://t.co/xBEMSF8Y72) surveyed 129 finance leaders at companies from $50M to$ 5B+ in revenue. 77% said they want to uplevel existing systems with AI from new vendors that layer onto existing systems. Only 15% want to replace their current system of record with an AI-native platform. The incumbent wins if they ship AI. The AI-native challenger wins only if the incumbent doesn't.

The signal shows up in our VoC data too. In regulated verticals, mission criticality scores cluster above 9, and NPS doesn't track satisfaction, it tracks switching friction. Customers will tell you the product is mediocre and still score it 9 on "would not switch" because the compliance team vetoes any alternative. 𝗧𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝘁𝘂𝗿𝗲 𝗼𝗳 𝗮 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲-𝗶𝗻𝘀𝘂𝗹𝗮𝘁𝗲𝗱 𝘃𝗲𝗻𝗱𝗼𝗿, 𝗮𝘀 𝗹𝗼𝗻𝗴 𝗮𝘀 𝘁𝗵𝗮𝘁 𝘃𝗲𝗻𝗱𝗼𝗿 𝗶𝘀 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗰𝘂𝗿𝘃𝗲.

Which brings us back to the second business for everyone outside the regulated or data-entrenched moat. Seat ARR got them to $100M. But with the shift to agentic workforce structures, partial human capital replacement, and pricing pressure compressing margins, the traditional SaaS model has to transform fast. The next$ 500M comes from monetizing the installed base: marketplace rake on demand they generate for their own customers, capital products underwritten by their own transaction data, supplier monetization, brand partnerships, group buying. The assets are already sitting there. Captive SMB audience. Proprietary transaction and behavioral data. A distribution pipe (the UI itself) that delivers new products at near-zero CAC.

𝗪𝗵𝗮𝘁'𝘀 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗶𝘀 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘄𝗶𝗹𝗹. Monetizing the installed base requires a different org than the one that got you to scale. Different GTM, P&L optics, and talent. Founders and boards under-invest because year one looks worse before it looks better, and public markets punish any SaaS multiple that starts to look like fintech or marketplace. So the second business never ships. The round prices in the optionality. The multiple compresses. The exit underwhelms.

𝗧𝗵𝗿𝗲𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀 𝗮𝗿𝗲 𝗮𝘀𝗸𝗶𝗻𝗴:

𝟭. 𝗪𝗵𝗮𝘁 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗼𝗳 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗼𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴? Under 5%, they haven't started. 10 to 20%, thesis is live. Over 20%, it's working.

𝟮. 𝗛𝗼𝘄 𝗵𝗮𝗿𝗱 𝘄𝗼𝘂𝗹𝗱 𝗶𝘁 𝗯𝗲 𝘁𝗼 𝗿𝗲𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗶𝘀 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵 𝘄𝗶𝘁𝗵 𝗔𝗜 𝘁𝗼𝗱𝗮𝘆? If a well-funded team with Claude and six engineers could rebuild the functional product in nine months, the software isn't the moat. The moat has to live somewhere else: proprietary data, a network, integrations, or regulatory surface area the challenger can't clear. If you can't point to at least one, you're underwriting a melting ice cube.

𝟯. 𝗪𝗵𝗮𝘁 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗯𝘂𝘆𝗲𝗿'𝘀 𝘀𝘁𝗶𝗰𝗸𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆, 𝗮𝗻𝗱 𝘄𝗵𝗶𝗰𝗵 𝘄𝗮𝘆 𝗶𝘀 𝘁𝗵𝗲 𝗿𝘂𝗹𝗲 𝘀𝗲𝘁 𝗺𝗼𝘃𝗶𝗻𝗴? A regulatory moat evaporates if the regulation simplifies. Underwrite the direction of travel, not just the current state.

𝗔𝗻𝗱 𝘁𝗵𝗲 𝗰𝗹𝗼𝗰𝗸 𝗶𝘀 𝘁𝗶𝗴𝗵𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗺𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗲. Retention in enterprise SaaS has largely been defined by the pain of systems replacement, not genuine moat. If the stickiness isn't backed by proprietary data, a harvesting flywheel, or regulatory surface area, those vendors are about to get disrupted. Pure seat-based pricing is dying unless vendors embrace agent-seat models, and LLM providers have been subsidizing the market on token cost, with recent pricing shifts signaling cash reserves aren't infinite.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘂𝗻𝗱𝗲𝗿𝗮𝗽𝗽𝗿𝗲𝗰𝗶𝗮𝘁𝗲𝗱 𝗽𝗼𝗶𝗻𝘁: 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝗵𝗮𝘃𝗲 𝘄𝗼𝗿𝘀𝗲 𝗴𝗿𝗼𝘀𝘀 𝗺𝗮𝗿𝗴𝗶𝗻𝘀 𝘁𝗵𝗮𝗻 𝗦𝗮𝗮𝗦 𝗶𝗻𝗰𝘂𝗺𝗯𝗲𝗻𝘁𝘀, 𝗻𝗼𝘁 𝗯𝗲𝘁𝘁𝗲𝗿. Inference costs haven't collapsed, and burning VC cash to subsidize unit economics is a bridge, not a business model. The incumbents should be winning on P&L. They're losing on product velocity and AI-readiness. That's a solvable problem if the board has the will to ship. Vendors without a second business, without a data moat, and without regulatory insulation will still lose, despite having better margins than their AI-native challengers. Customers switch on features and speed, not on unit economics.

𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗲𝗱 𝘃𝗲𝗿𝘁𝗶𝗰𝗮𝗹𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝘀𝗮𝗳𝗲 𝗵𝗮𝗿𝗯𝗼𝗿, 𝗮𝗻𝗱 𝗼𝗻𝗹𝘆 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗯𝗿𝗲𝗮𝗱𝘁𝗵 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲. Everywhere else, the premium is about to get competed away. Any fund underwriting vertical SaaS exposure right now should be asking the second-business question before the next check clears. DM me, email me [email protected], or let's chat about your portfolio/underwriting process (https://t.co/muMNtk6ssk). https://t.co/ElZm7vjalx

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@startupideaspod: RT @gregisenberg: There’s $1T up for grabs for agent-first startups and this window is WIDE open. Pr...
@rickasaurus: Must read\n\nQT @blyons151: In August I wrote a thesis I never published. The funds I was warning we...

4. CopilotKit (Group Score: 112.7 | Individual: 34.5)

Cluster: 4 tweets | Engagement: 168 (Avg: 23) | Type: Tech

RT @googledevs: A2UI v0.9 is the new standard for portable Generative UI, allowing your agents to natively "speak" UI directly to your existing frontends.

✨ React, Flutter, and Angular support 🐍 Python SDK via pip install ⚡ High-performance streaming 🛠️ Design system integration

Learn more via the blog: https://t.co/YOTjKGBDjm

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@CopilotKit: Proud to be launch partners & contributors to A2UI v0.9!\n\nQT @googledevs: A2UI v0.9 is the new...
@CopilotKit: Pay attention to this launch 👀

The full spectrum of Genrative UI capabilities is maturing.

Copilot...

@CopilotKit: RT @ataiiam: Today, we're joining Google's release of A2UI v0.9 🎉

Declarative Generative UI with yo...

5. rauchg (Group Score: 103.7 | Individual: 38.3)

Cluster: 3 tweets | Engagement: 1043 (Avg: 1336) | Type: Tech

Whether design belongs in Figma or Claude Design is a distraction from a bigger shift.

1️⃣ Design will become autonomous. More helpful to think of it as 𝙳𝙴𝚂𝙸𝙶𝙽.𝚖𝚍, used by your coding agents running your software factory.

2️⃣ Specialized “personal” design tools generated by teams will proliferate. Design is a capability, not a tool. I agree with @rsms that there are many facets of design, and multiple tools are required.

I love prompting in @v0 and it’s become the place where I can channel my inspiration, explore, communicate. But I’m also seeing a new generation of products that use the v0 Platform API or Sandbox and put design on autopilot.

There are next-generation agents like @tryflint and https://t.co/fZ7TO6rczt generating design & brand systems and maintaining them autonomously. Flint can even keep your website and content up to date and its design consistent. No human prompting needed.

From this we will see the emergence of fully autonomous companies with agents like https://t.co/YhOmGYYwgE and https://t.co/v8TuIxujwy, which go a step further and grow and advertise your business.

tl:dr; The future looks very different from the present. AI is a true discontinuity. The “here’s the existing thing but with AI and ${jobTitle} is cooked” is short-sighted.\n\nQT @rauchg: Today we're open sourcing https://t.co/p76KVdY7dG, a reference platform for cloud coding agents.

You've heard that companies like Stripe (Minions), Ramp (Inspect), Spotify (Honk), Block (Goose), and others are building their own "AI software factories". Why?

1️⃣ On a technical level, off-the-shelf coding agents don't perform well with huge monorepos, don't have your institutional knowledge, integrations, and custom workflows.

2️⃣ On a business level, the moat of software companies will shift from 'the code they wrote', to the 'means of production' of that code. The alpha is in your factory.

Open Agents deploys to our agentic infrastructure: Fluid for running the agent's brain, Workflow for its long-running durability, Sandbox for secure code execution, AI Gateway for multi-model tokens.

(Because of our focus on Open SDKs and runtimes, this codebase is a gem even if you're not hosting on Vercel.)

TL;DR: if you're building an internal or user-facing agentic coding platform, deploy this: https://t.co/xdsc42nbDN

See 2 related tweets

@mattturck: RT @rauchg: Whether design belongs in Figma or Claude Design is a distraction from a bigger shift.

...

@levie: When people think software engineers, they tend to think jobs at tech companies building apps. The g...

6. minchoi (Group Score: 102.4 | Individual: 47.9)

Cluster: 4 tweets | Engagement: 1610 (Avg: 277) | Type: Tech

Anthropic shut down an entire company's Claude access overnight

60+ employees. No explanation. Just an email.

Want to appeal? Fill out a Google Form.

Integrations gone. Histories gone. Everything built on Claude... gone.

Never let one vendor own your workflow. https://t.co/196QCCnB4D\n\nQT @patomolina: Anthropic decidió dar de baja a toda nuestra organización por una supuesta infracción de sus condiciones de uso. Qué política específica infringimos no tengo ni la menor idea: simplemente recibimos un mail y listo, adiós Claude. Si querés apelar la medida hay que completar un Google Form, así de ridículo como suena.

De golpe más de 60 personas se quedaron sin una herramienta fundamental para trabajar. Integraciones, skills, historial de conversaciones: todo perdido o, en el mejor de los casos, parado por tiempo indeterminado.

Enorme aprendizaje para cualquier empresa de software que dependa de herramientas de IA en procesos críticos. Nunca hay que poner todos los huevos en una canasta.

See 3 related tweets

@edzitron: Anthropic really does abuse their customers lol\n\nQT @patomolina: Anthropic decidió dar de baja a t...
@rickasaurus: RT @patomolina: Anthropic decidió dar de baja a toda nuestra organización por una supuesta infracció...
@10_X_eng: Clankie has access to them all.\n\nQT @patomolina: Anthropic decidió dar de baja a toda nuestra orga...

7. seraleev (Group Score: 101.9 | Individual: 38.6)

Cluster: 3 tweets | Engagement: 92 (Avg: 43) | Type: Tech

Advertising is the engine.

Whoever masters Meta ads / Google ads / TikTok ads / ASA will rule the game.

I’ve burned tens of thousands of dollars on ad tests. It’s not a fast process. And yes, I was very close to declaring myself bankrupt.\n\nQT @alexcooldev: I really like people who show the truth like Adam.

The reason I don’t run ads something people often ask is because my app is for students and follows a freemium model, so there’s a high chance of losing money if I run ads.

I actually tested Meta Ads with around $1k, and my cost per install was about$ 7 (probably because my skills aren’t great lol). The revenue I got back was only around $50 bruh, I’d go bankrupt if I kept testing and failing like that.

And even if you are profitable, you still have to subtract another 15%–30% fee from Apple. You need to burn a lot of cash for the algorithm to learn before you can become profitable. Plus, your app has to convert extremely well like those looksmaxing or height-growth apps with hard paywall.

Ads are actually great if you’re VC-backed or have a big budget.

See 2 related tweets

@alexcooldev: I really like people who show the truth like Adam.

The reason I don’t run ads something people ofte...

@alexcooldev: I agree with this point. Another reason I don’t run ads for my mobile app is that I already have to ...

8. Austen (Group Score: 90.8 | Individual: 49.4)

Cluster: 3 tweets | Engagement: 3347 (Avg: 369) | Type: Tech

Honest question:

Why does he keep saying this?\n\nQT @TFTC21: Anthropic CEO Dario Amodei: “50% of all tech jobs, entry-level lawyers, consultants, and finance professionals will be completely wiped out within 1–5 years.” https://t.co/sXcT59gWYj

See 2 related tweets

@TheAhmadOsman: Does this guy EVER have anything else to say???\n\nQT @TFTC21: Anthropic CEO Dario Amodei: “50% of a...
@Hesamation: he won’t have a good sleep at night if he doesn’t remind the public every month that they must fear/...

9. OpenAIDevs (Group Score: 72.6 | Individual: 54.6)

Cluster: 2 tweets | Engagement: 1213 (Avg: 142) | Type: Tech

You can just build things.\n\nQT @Baconbrix: Building an iPhone app directly in Codex desktop with iOS simulator https://t.co/jZSOouHDur

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@Scobleizer: RT @Baconbrix: Building an iPhone app directly in Codex desktop with iOS simulator https://t.co/jZSO...

10. BrianRoemmele (Group Score: 69.3 | Individual: 35.5)

Cluster: 2 tweets | Engagement: 143 (Avg: 350) | Type: Tech

You should be angry that a clueless bunch of nihilists are running the narrative about how AI will play out by feeding a doomer spiral that hurts you and I in ways that we can’t calculate.

Fermenting a rising anger that will flood the streets in 2028, setting us back decades.\n\nQT @BrianRoemmele: The multilevel marketing system called Anthropic have built an Effective Doom Cult and now have it sock puppeteed across the world.

The genius that thought this IPO prequel EMDASH has not a clue how it injured the company and the industry.

THANK YOU. https://t.co/jhvOu3m0Mg

See 1 related tweets

@BrianRoemmele: The multilevel marketing system called Anthropic have built an Effective Doom Cult and now have it s...

11. heyshrutimishra (Group Score: 68.5 | Individual: 38.1)

Cluster: 2 tweets | Engagement: 178 (Avg: 43) | Type: Tech

RT @TEDTalks: “The lobster is loose, and it’s not going back into the tank,” says @openclaw founder @steipete. In this brand new talk from #TED2026 he shares why AI agents — built by you — are the future: https://t.co/GnR6fTeRKw https://t.co/W6gywZ7uYh

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@Hadas_Gold: A TED talk!\n\nQT @TEDTalks: “The lobster is loose, and it’s not going back into the tank,” says @op...

12. amasad (Group Score: 67.9 | Individual: 34.0)

Cluster: 2 tweets | Engagement: 5 (Avg: 455) | Type: Tech

Important learning opportunity. Could be transformative for your business/career.\n\nQT @jasonlk: It's time to learn to Build it. Ship it. Vibe it. Get it into production. For real. We'll make you an agentic expert.

Together with @Replit at 2026 https://t.co/PBv85S5Cyt May 12-14 we'll teach you:

-How to Build Your Own AI VP Marketing

How to Build Your Own AI VP Customer Success
How to Ship AI-Powered Sales & Marketing Tools in 30 Min
How to Turn a Mockup into a Working Prototype
How to Go From Prompt to Product in 30 Min
How to Build Your Own AI-Powered MVP

No code required. Just bring your laptop.

We'll give you the prompt.

https://t.co/PBv85S5Cyt 2026. May 12-14 in SF Bay!!

See 1 related tweets

@jasonlk: It's time to learn to Build it. Ship it. Vibe it. Get it into production. For real. We'll make ...

13. chandrarsrikant (Group Score: 67.9 | Individual: 35.7)

Cluster: 2 tweets | Engagement: 81 (Avg: 515) | Type: Tech

On YC startups flipping to US and paying back taxes while flipping back

"We wish that we could just fund Indian companies. We would love to do that. But the Indian government makes it very challenging for US based investors to fund Indian entities. It's not only a problem for us, but also a problem for all the US-based investors that would follow Y Combinator and invest in the companies after us. I wish I had a solution to this problem. But what I can say is that in the meantime, the fact that more and more of the companies coming from India are building for the global market suggests to me that that might be at least a partial solution, which is that if you're building a company like Emergent or Giga, actually the headquarters are in the US, even if a lot of the headcount is in India, and the customers are all in the US and the investors are mostly in the US, maybe it makes more sense for you to be a US entity. And the fact that we can't fund Indian entities is less of a problem than it was in the past."\n\nQT @moneycontrolcom: #MCInterview | MC Interview: India has AI talent but lacks breakout ideas, says Y Combinator's Jared Friedman

@chandrarsrikant & @Goenka_Tushar1 with more details⏬ https://t.co/4lisim2idE https://t.co/4JTz3RHKki

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@chandrarsrikant: As @snowmaker prepares to host Y Combinator’s first-ever Startup School India at a massive (wedding?...

14. seraleev (Group Score: 67.1 | Individual: 40.0)

Cluster: 2 tweets | Engagement: 174 (Avg: 43) | Type: Tech

Haven’t submitted a new app in 3+ months. So this tweet from Max was actually useful. When submitting a new app, Apple asks for:

Screen recording on a physical device showing core user flows
App purpose and value description
Instructions + test credentials to access features
List of external services/SDKs used
Regional differences or confirmation there are none
Regulatory credentials if applicable\n\nQT @maks6361: Looks like Apple has introduced its answer to the huge wave of AI-built apps.

I started getting this rejection just 30–40 minutes after the initial submission of a new app. https://t.co/YTtoDqcaQQ

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@adamlyttleapps: Also be mindful: this will probably act as an archive for how your app behaves

If you change the on...

15. Origin_AI_01 (Group Score: 66.8 | Individual: 34.4)

Cluster: 2 tweets | Engagement: 116 (Avg: 365) | Type: Tech

Speed isn’t a feature in conversational AI, it’s the product.

LemonSlice didn’t just claim faster avatars, they actually proved it with real benchmarks.

That’s how you move from demo to adoption.\n\nQT @LemonSliceAI: We did it! We built the fastest interactive avatar model

Introducing LemonSlice-2.1 𝘍𝘭𝘢𝘴𝘩 ⚡

Here’s how we did it using @modal and @livekit 👇 (note: it was not easy) https://t.co/iZcm1BzqJ9

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@Parul_Gautam7: this isn’t just about speed

> shaved milliseconds across the entire stack >DiT, VAE, CUDA, al...

16. martin_casado (Group Score: 64.0 | Individual: 32.5)

Cluster: 2 tweets | Engagement: 66 (Avg: 123) | Type: Tech

This is such a dumb and ill informed take.

There are a lot of funny games in startup reporting, creative annualized run rates, passing off GMV as ARR, gross margin shenanigans, etc.

But using exit ARR as current is both not that common, and generally less problematic.\n\nQT @scottastevenson: It’s time to expose a huge scam in AI startups: Contracted ARR

The reason many AI startups are crushing revenue records is because they are using a dishonest metric

The biggest funds in the world are supporting this and misleading journalists for PR coverage.

The setup: Company signs 3-year enterprise deals. Year 1 is discounted (say $1M), Year 2 steps up ($ 2M), Year 3 is full price ($3M).

They report $3M as “ARR” — even though they’re only collecting$ 1M right now.

The worst part: The customer has an opt-out option at 12 months! It’s not actually a 3 year contract.

In the chart below, by Q5 the company is trumpeting ~ $100M “ARR” to press, while actual cash-generating, in-effect ARR is ~$ 35M. That’s ~3x inflation.

On top of this, enterprise AI companies are bundling full-time “forward deployed engineers” into deals massively reducing margins, sometimes producing Year 1 negative margins.

At some point customers are going to start triggering their opt-out clauses or aggressively negotiating down Year 3 pricing.

And a wave of enterprise AI companies may collapse.

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@edzitron: This is very interesting but without names to attach it to it’s hard to measure! But if it’s anyone ...

17. RoundtableSpace (Group Score: 62.6 | Individual: 38.9)

Cluster: 2 tweets | Engagement: 1280 (Avg: 201) | Type: Tech

RT @RoundtableSpace: THIS GUY JUST DROPPED A 16 MIN TUTORIAL ON USING GEMINI 3.1 + SEEDANCE 2.0 TO BUILD CINEMATIC $10K WEBSITES

https://t.co/AinuKm5gkn

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@RoundtableSpace: A user just dropped a full tutorial on building a high end cinematic website with Gemini 3.1 and See...

18. burkeholland (Group Score: 62.4 | Individual: 43.9)

Cluster: 2 tweets | Engagement: 1070 (Avg: 103) | Type: Tech

RT @JessicaNutt96: Mythos is not a bad name for a model but it would be better if Anthropic switched to using famous Claudes. Monet, Debussy etc. The final model that achieves AGI would obviously be Van Damme

See 1 related tweets

@martin_casado: The closest thing I’ve seen to a perfect tweet\n\nQT @JessicaNutt96: Mythos is not a bad name for a ...

19. MarioNawfal (Group Score: 61.6 | Individual: 25.2)

Cluster: 3 tweets | Engagement: 120 (Avg: 684) | Type: Tech

xAI just made Speech to Text available to everyone.

25 languages. Batch uploads.

Live streaming.

Word-level timestamps.

Multichannel audio.

Speaker diarization.

That last one is usually locked behind enterprise tiers at OpenAI and Google.

@tetsuoai @elonmusk https://t.co/ZPZ1o07ZmG\n\nQT @MarioNawfal: 🇺🇸 xAI just dropped a real-time speech-to-text model built for voice apps: high limits, multi-language support, the works.

Priced at just $0.10–$ 0.20/hour, crushing most competitors.

Quiet release, loud warning to the rest."

Source: @XFreeze, @xAI, @elonmusk https://t.co/OAvpLK4Tuv

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@XFreeze: xAI just launched their Speech-to-Text (STT) API and it's insanely powerful 🎙️

It's the most accura...

@cb_doge: xAI just launched Grok Speech-to-Text and Text-to-Speech APIs.

Grok Speech-to-Text

• 25+ languages...

20. alexandr_wang (Group Score: 61.5 | Individual: 34.0)

Cluster: 2 tweets | Engagement: 260 (Avg: 404) | Type: Tech

Thank you @RonConway for accelerating technology, the future, and humanity. F*ck cancer.\n\nQT @paulg: "Ron discovered how to be the investor of the future by accident. He didn't foresee the future of startup investing, realize it would pay to be upstanding, and force himself to behave that way. It would feel unnatural to him to behave any other way."

https://t.co/HfzO3iMRcg

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@paulg: "Ron discovered how to be the investor of the future by accident. He didn't foresee the future of st...