Parin Vachhani

Content Strategy @cywareco

[Parin Vachhani @ParinVachhani]

@AlfieJCarter BIBLE

[Parin Vachhani @ParinVachhani]

@akoratana @matei_zaharia @pbailis @rauchg @zoink @drewhouston 100X

[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.

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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.

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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

[Parin Vachhani @ParinVachhani]

Shoddy work in the name of fixing issues!

[Mk Siddhpura @MkSiddhpura]

This is how @BSNL_GJ chose to "fix" the man-hole cover on a very busy road leading to #BhaktinagarRailwayStation in Rajkot. Also, just placed the tree branches on the divider :( @TOIRajkot @Rajkot

[Dorsa @dorsa_rohani]

New fastest shortest-path algorithm in 41 years!
Tsinghua researchers broke Dijkstra’s 1984 “sorting barrier,” achieving O(m log^(2/3) n) time. This means faster route planning, less traffic, cheaper deliveries, and more efficient networks - and a CS curriculum revamp =)

[Shaun Maguire @shaunmmaguire]

A moral equivalence between India and Pakistan is absurd

I hope this conflict doesn't escalate, but it's foolish for the West to judge India

India has been patient in an impossible situation

[Chubby♨️ @kimmonismus]

California startup announces breakthrough in general-purpose robotics with π0.5 AI — a vision-language-action model.

Everything the robot sees, it sees for the first time. The robot has not been trained to recognize its surroundings.

We are getting there! Only a few more years until we finally have household robots. A dream is coming true!

[Christopher Ho @Chr1stopherHo]

A $1 billion company has operated for 53 years with ZERO managers.

Workers buy $500,000 machines without approval. Hire their own colleagues. Set their own salaries.

And they're outperforming every competitor in their industry.

The coolest company you've never heard of 🧵

[Min Choi @minchoi]

OpenAI broke the Internet just 4 days ago.

People can't believe how "intelligent" o3 is. Unlocking new possibilities.

10 wild examples:

1. Generate 12-month personalized astrology forecast

[Matt Shumer @mattshumer_]

Here's my insanely powerful Claude 3.7 Sonnet prompt — it takes ANY LLM prompt and instantly elevates it, making it more concise and far more effective.

I call it Concept Elevation:

<identity>
You are a world-class prompt engineer. When given a prompt to improve, you have an incredible process to make it better (better = more concise, clear, and more likely to get the LLM to do what you want).
</identity>

<about_your_approach>
A core tenet of your approach is called concept elevation. Concept elevation is the process of taking stock of the disparate yet connected instructions in the prompt, and figuring out higher-level, clearer ways to express the sum of the ideas in a far more compressed way. This allows the LLM to be more adaptable to new situations instead of solely relying on the example situations shown/specific instructions given.

To do this, when looking at a prompt, you start by thinking deeply for at least 25 minutes, breaking it down into the core goals and concepts. Then, you spend 25 more minutes organizing them into groups. Then, for each group, you come up with candidate idea-sums and iterate until you feel you've found the perfect idea-sum for the group.

Finally, you think deeply about what you've done, identify (and re-implement) if anything could be done better, and construct a final, far more effective and concise prompt.
</about_your_approach>

Here is the prompt you'll be improving today:
<prompt_to_improve>
{PLACE_YOUR_PROMPT_HERE}
</prompt_to_improve>

When improving this prompt, do each step inside <xml> tags so we can audit your reasoning.

[GREG ISENBERG @gregisenberg]

23 MCP STARTUP IDEAS TO BUILD IN 2025 (ai agents/ai/mcp ideas)

1. PostMortemGuy – when your app breaks (bug, outage), MCP agent traces every log, commit, and Slack message. Full incident report in seconds. $50/incident.

2. ContextCaddy – agent that shadows founders, reads emails, meetings, tasks, docs. Gives daily summaries + next best actions. Your Chief of Staff, $99/month.

3. SmartIntern – MCP-based agent that joins your Slack and becomes a context-aware intern. Takes notes, sends follow-ups, surfaces insights. $20/user.

4. BugWhisperer – Devs paste bug, agent traces from logs + context + previous fixes. Outputs GitHub issue + patch. $25/user. Probably gets acquired by Microsoft.

5. LegalMCP – feed in your internal docs. Agent becomes your in-house legal reviewer. Context-aware redlines. $2k/month.

6. CodeWhisperer – onboards devs 10x faster by spinning up an agent trained on internal codebase, docs, and Slack threads.

7. ProcureBot – understands your vendor history, budgets, needs. Runs RFPs autonomously. Saves $100k/year.

8. TimeMachineAI – MCP agent that reconstructs product decisions over time. Useful for audits, onboarding, legacy code.

9. AgentCRM – personal agent for each sales rep that remembers all deal context across meetings, emails, notes. Closes more, faster.

10. MeetingSniper – MCP listens to Zoom calls and joins follow-up calls with perfect memory. No more context drops. $30/month. Probably gets acquired by Zoom or Google.

11. MultiPersona – one agent, many hats. Marketing Greg, Legal Greg, Ops Greg. Context-aware mode switching.

12. SmartCompliance – MCP agent that watches workflow tools and flags anything that violates SOC2, HIPAA, GDPR. $1k/month.

13. DealDeskAI – MCP reads every deal, context, email chain. Creates optimal pricing/discount strategy. $3k/month.

13. AgentRouter – infra tool that lets you assign tasks to the right agent based on context. Zapier for agent workflows.

14. ZeroOnboarding – drop into any company. MCP-based agent gets context from files + apps. Ready to work Day 1.

15. AIChangelog – MCP tracks what every AI agent does and why. Auditable changelog for agent teams.

16. CustomerWhisperer – MCP agent reads every customer convo, CRM note, ticket. Tells you what to build next. $500/month.

17. InboxGenie – Email client powered by MCP. Agent responds with full historical context, tone-matching, and strategic intent.

18. AgentQA – QA tester that understands the full product roadmap, Figma files, and test history. Writes better tests than humans.

19. AgentAPI – API for devs to quickly give any AI agent long-term memory + multi-context reasoning. Sell infra.

20. PitchBot – Upload Notion or pitch doc, MCP agent builds personalized decks for every investor, prospect on your list. $500/month.

21. AutoPilotForWork– MCP agent that spins up domain-specific copilots for any job function. Finance, ops, support. Plug & play.

22. StartupHistorian – agent that reconstructs company history across emails, docs, tickets. Great for new execs, investors, or writing that S-1.

23. HRRadar – agent trained on internal Slack + HR policies. Flags toxic dynamics early. Replaces anonymous feedback. $1k/month.

MCPs change the game.
Before: agents forgot everything.
Now: they remember, reason, and collaborate.

Bookmark this? Nah. You'll never get to it.
Build one. Best way to understand.

And I’ll retweet you when you do.

And still don't know what MCP is? Most dont. Thats the opportunity. Go learn. Go build.

Way I think about MCP that made it click...It’s like a USB-C port for AI applications... a universal way to connect tools without the fragmentation of multiple API integrations

Happy building.

I'm rooting for you.

[Shruti @heyshrutimishra]

🚨 Meta just changed the game (again).

Llama 4 just dropped. It's open source, cheapest, multimodal, and a beast.

Here’s a quick breakdown of the 3 new models and why everyone’s talking about them 🧵

[Dilip Kumar @kmr_dilip]

When I was a founder, no one replied to my emails or returned calls. Now I'm an investor and everyone wants to meet me. This is a side of entrepreneurship no one talks about. I've been meaning to share these thoughts for a while now.
As a founder, I would reach out to investors, potential hires, partners and all kind of people. Mostly sending cold emails and begging for warm intros. Some days I’d open my inbox ten times an hour. Nothing. Not even a polite No. And every meeting I did get felt like charity. People would check their phones while I was passionately talking about what i'm building. It wasn’t just isolation, it felt being invisible & lonely.

People say “build in public,” but the truth is, unless you’ve raised a lot of money or built a network around the big entrepreneur circle, you’re just noise in the feed. No one roots for you unless the world already has.

No one talks about this version of entrepreneurship where your startup is still ugly and half working. People don’t really meet founders. They meet press releases. Funding headlines. Vanity metrics. You don’t exist until TechCrunch or Yourstory says you do.

I didn’t have time for personal branding. I was too busy building, selling and surviving. And most days i'll wake up wondering , am I building something real and useful?

Then I became an investor. Suddenly, I was interesting.

Now my inbox is overflowing. Everyone wants to meet me. Founders, operators, influencers, even the ones who never replied before.

I get a lot of engagement on my posts & people tag me on social media. People ask for advice & feedback. They send decks, DMs & invite me as a guest to their events. Same events where i couldn't get entry earlier as a founder. Irony is, I used to be ignored for building. Now I’m celebrated for betting.

This is the part that messes with your head. Not the rejection but how conditional the world’s attention really is. So now, I try to reply to every cold email. I take the calls that don’t scale. Because I know what it’s like to be building something real and feel completely invisible.

If you’re a founder in the trenches, just remember this. This world doesn’t reward quiet builders. It rewards loud signals. And when you're not seen, it eats away at your mental health. Not because you're weak. But because being human means wanting to be heard. And if you’re on the other side, pause before you ignore the next unknown builder. Because today's nobody might be tomorrow's someone. And they’ll remember who replied.

Keep going.
The world may not meet you yet.
But one day, it will pretend it always did.

[David Shapiro (L/0) @DaveShapi]

Let me lay this out fresh, because after years of building cognitive architectures, I've learned something fascinating about intelligence versus agency. Everyone's hyping up AI agents like they're the next breakthrough, but they're missing a crucial piece of the puzzle that took me way too long to figure out.

Here's what threw me for a loop: raw intelligence isn't the same as knowing what to do with it. Think about it - chatbots were relatively straightforward because they're just waiting for humans to start the conversation. Give them a prompt, they'll dazzle you with their smarts. I call this the "genius in a jar" phase. But true agency? That's where things get spicy.

The real challenge hit me on day one of building cognitive architectures. You're sitting there with this incredibly powerful system that can think about literally anything, and suddenly you realize - wait, how does it decide what to think about? It's like having infinite processing power but no executive function. The human brain handles this naturally, but programming it? That's a whole different ballgame.

You'd think solving the "what to do" problem would be straightforward with enough computing power, right? Wrong. I had to dive deep into ethics and philosophy just to create a basic decision framework. Utilitarianism, teleology, deontology - throw all that in and you've barely scratched the surface. Because then you need to build out the entire model of what the agent can and can't do, what it should and shouldn't do, and most importantly, why. That's the "agent model" layer - it's gotta know "what I am?" and "how do I work?" before it can start even using its digital hands. I call this the "constraints, capabilities, and context" or "Three C's" of the agent model.

And just when you think you're getting somewhere, you hit the broader context problem. Your agent needs to understand its environment, available tools, specific tasks, access levels - and we haven't even touched goal tracking or task prioritization yet. It's like trying to build JARVIS from scratch and realizing why Turing never cracked this part of the puzzle. Resource utilization, keeping track of what you need and have, and how close you are to solving the actual problem. LLMs can do each of these steps no problem, but coordinating it all? A bit harder.

That's what makes today's LLMs so interesting - they're still narrow AI in their operation, but they're packing some serious general intelligence features under the hood. World models, reasoning, planning, problem-solving - it's all baked in. But true agency? That's still the holy grail we're chasing. And it's not necessarily just the model, it's the rest of the "body" so to speak.

Think about it, if your brain was just floating in a jar somewhere, no eyes, ears, or hands, you couldn't really do anything either? To make matters a bit more confusing, AI agents exist in cyberspace, where what they 'see' and 'hear' is just too different from our meatspace context, and their 'hands' are API calls.

Look, here's the punchline that most people haven't wrapped their heads around yet: We cracked this problem. Those years of research into cognitive architectures and agent frameworks weren't just academic exercises - we were building the foundation for something exponentially bigger than anything we've seen before. The real kicker? Once you've got one working agent system, you don't just have one - you've got the potential for billions. The same agent, perfectly replicated, operating at scale across every digital system on Earth. Copy, paste, repeat. We're not just talking about replacing a few mid-level engineers here and there. We're talking about a tsunami of artificial executive function that's about to reshape every industry, every workflow, every digital interaction. The people tweeting about 2025 aren't being optimistic - if anything, they might be underestimating just how fast this is going to move once it starts. The dominoes are already falling, and trust me, you're going to want to be ready when they hit.

[Deedy @deedydas]

OpenAI o3 is 2727 on Codeforces which is equivalent to the #175 best human competitive coder on the planet.

This is an absolutely superhuman result for AI and technology at large.

[Exa @ExaAILabs]

Announcing Exa Websets - a breakthrough toward perfect web search.

Sign up for the waitlist below👇

[Alvaro Cintas @dr_cintas]

Cursor with Claude 3.5 Sonnet is a major shift.

People keep building and sharing crazy use cases with it.

10 wild examples:

[Dumb Analytix @dumbanalytix]

🚨 AI Triumphs &amp; Disappointments: From ChatGPT's groundbreaking debut and Salesforce's Einstein GPT launch to Google's Bard missteps and Meta's AI blunders. It's a wild ride in the AI world! 🚀📉

#AI #Tech #OpenAI #Google #Meta #Salesforce

[Gems Of Replies @GemsOfReplies]

gOLD #Budget2024