ranaq

I work at the intersection of People & Tech & Analytics. Host a fun HR Podcast at -

[Peter Yang @petergyang]

5 steps to create amazing videos like the one below from HTML for free:

Install @HyperFrames_ in your favorite coding harness (e.g., Codex or Claude Code).

Then...

1. Gather your assets

Create a project folder and add screenshots, logos, website captures, reference clips, and a frame md to give Hyperframes the raw material for your video.

If you have a design md you can convert it to a frame md here: https://t.co/3l6bT1BA9Q

📌 Watch @JakeFromHeyGen cover all 5 steps live: https://t.co/3VYURHutAf

...or keep reading below 👇

[SendShort 💛 @SendShort]

Clip the most viral videos with https://t.co/Ewkvp804q8 and make a living out of it, starting today.

[Sudhakar R @iSudhakarIndian]

@SirPareshRawal And this curated version https://t.co/gTlu3UUGCI may help latest generation to understand our history, culture and traditions of Bharat, in their style.

[Shiv @Sh1v4_3]

@mgnayak5 Here's the advanced version from 1904, from the same institution. https://t.co/cT2jjiQzKO

[Romàn @romanbuildsaas]

Here's the exact Gojiberry AI pitch we used for Demo Day with the P26 batch at @ycombinator.

It helped us get our fundraising round almost fully committed in less than a week.

It's not an easy thing to do, especially with more than 1,000 people in the audience.

[paolo trivellato @paolo_scales]

https://t.co/WafDaBDXMk

[Chandu Thota @ChanduThota]

Video is an effective way to communicate, and we want to make it as easy as editing slides - that's why we created Google Vids (https://t.co/Z0lp7dvIRB).

We are launching major enhancements to AI avatars, voiceovers, and video generation in Google Vids.

Using these new updates, you can:

- Turn Google Slides presentations into engaging videos
- Craft awesome videos across 24 languages, including Hindi :)
- Demo your products and services with custom avatars

More details on this and more below...

🧵 1/5

[Nicolas Finet @nifinet]

https://t.co/vIIOeQAONZ

[Cam Wright @thecamjwright]

https://t.co/WYhNoWYtS8

[Saurav @saguppa]

Here’s how i generate 1,000s of high-intent leads from LinkedIn per month (both manual and automated)

I run SalesRobot, a LinkedIn automation tool at $80K MRR.

Here's the manual method I started with, why it wasn't enough, and what I built instead.

First, the manual method.

Find a LinkedIn post where your ICP is already engaging. Could be a competitor's post, an influencer in your niche, or a viral thread about the exact problem you solve.

Paste the URL into SalesRobot. It scrapes every commenter and imports them as a campaign audience directly. That's it.

A comment on a relevant post tells you more about buying intent than any filter Sales Navigator has.

The reply rates show it clearly:

Cold outbound on a standard Sales Nav list: 28% acceptance, 43% reply rate.

The same message sent to a comment scraped list: 56-70% acceptance, 40-47% reply rate.

But here’s the problem I was facing with doing it only manually.

You're dependent on finding relevant posts at the right time. Plus you're also capped at whatever posts happen to go viral in your niche that week.

So I built the automated version of it.

I use Apify to run a script on Render as a cron job (Claude Code wrote it of course)

It scrapes viral posts from the common suspects who my ICP follows (typically influencers who post a lot of cold email advice)

Once the post URL is scraped, I use the SalesRobot APIs to automatically create a campaign targeting people who commented.

(Obviously, only if they are qualified using another script on Render. Sorry Clay )

The sequence looks like this:

Step 1 : blank LinkedIn connection request

Step 2 : Message "Noticed you liked this post by XYZ {{postUrl}}" (and then I also put the whole post link so that it looks like a genuine message because Linkedin shows the entire post preview)

Step 3 : Message "no sales pitch, just curious: what are you using for LinkedIn outreach these days?"

About 1,000 net new leads per month.

16% net reply rate from complete strangers.

The whole system generates $22,000/month in pipeline at $200/month in tool costs (I haven't taken the Claude Code Max plan yet)

The best leads aren't in a database. They're already telling you who they are by what they engage with.

Do what you want with this information. :)

[Garry Tan @garrytan]

If you need a website, this is the best website creator on the planet: Ploy by Bryant Chou

Drop everything and use this to improve your website now. A total redesign to your taste is at your fingertips. Try it with a side project and you'll bring it to your main project in 2 weeks or less, I predict.

[Ojasvika Sahu (Oz) @Ozacle23]

we just built what @satyanadella is talking about.

here's the ultimate COMPANY BRAIN, your sovereignty 👇🏼

try here- https://t.co/p4xkDWmefL

minimi feeds Claude every tab, doc, call, and thread on your mac. add your team-mates' links and claude knows their work as well. 💫

[Hila Shmuel @HilaShmuel]

“Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use”

sounds like they all need… https://t.co/AldlhNBTXr

[Jatin Sapra @jatinsapra]

I can retire now from Vietnam recommendations job. Ankit bhai has created the perfect Vietnam itinerary 🥹

[Guillermo Rauch @rauchg]

HTML is so back.
Drag and https://t.co/HJSiShgTtP

[Kat McGuire @katmcguir3]

marketing department 2.0 is live!

[Leo Mehr @LeoMehr]

Services are the future. Today we launched Ramp’s AI services motion.

It's easy to buy an AI subscription. It's hard to transform your company to actually run on agents.

Here’s our entire strategy.

1) Why now

Services are the new software (Sequoia)

Human labor TAM >> software license TAM. The market is bearish on seats and subscriptions.

Every enterprise AI company is doing this -- the labs have poured billions into services partnerships and their own deployment functions. Superintelligent models alone are not enough.

Palantir proved this is a strong business model: deeply embed engineers, build on top of a powerful platform, and customize extensively.

2) The real problem

Companies want AI. But the gap between "we have AI tools" and "agents run our workflows and we spend way less time" is enormous.

What we've found across over 50 companies we engaged with: agents start replacing real work when there is: complete data, read/write access across systems, agent-friendly policies. Most big companies struggle because:
- processes live in operators' heads
- dozens of disconnected systems (legacy ERPs, endless one-off excel sheets, etc.)
- archaic software with poor or no API access

Good data in the right place is a hard prereq to working agents.

Also, vibing in localhost ≠ a production system your enterprise can rely on. You still need hosting, ci/cd, observability, feedback loops, good interfaces. And taste to know what's even worth automating.

Everyone has a bulldozer, but most jobs just need a shovel pointed at the right spot.

What companies usually need is to be made agent-friendly. That's exactly what we do.

3) What we do

We focus on what Ramp does best -- finance.

And we embed FDEs that:
-> understand your problems
-> identify high-leverage, high-impact workflows that fit agents
-> scope the solution
-> connect your data
-> capture your context
-> deploy agents and often bespoke software for humans to collaborate with them
-> drive the business metrics that matter

Discovery and scoping are crucial. Building is easier than ever and thus judgement about what to build is more important than ever.

We're not a generic AI services arm, we're finance domain experts. Across the spectrum of financial operations, we help companies find and frame the problems worth automating -- similar to the taste a founder has in choosing which problems are worth solving (ex-founders make great FDEs).

Here’s the stack we deliver:

- Production infrastructure. Shipping an index.html from Claude isn't the same as creating a repo, hosting in a cloud service, ci/cd, testing, setting up evals, managing memories and skills, adding feedback loops, ensuring uptime, incident management, etc. Agents don't one-shot production systems yet. Production software is hard -- we build, host, and run it for you in a single-tenant, dedicated cloud environment. Most operators don’t have the time, knowledge, or experience to do this e2e. We help abstract the low-leverage plumbing so they can focus on the essential parts of their jobs.

- Data connectivity. Most enterprises have data lakes, but data is often incorrect, stale, or entirely missing. And write interfaces vary dramatically. Ideally we can use MCPs or CLIs, but usually it’s poorly documented APIs, SFTP, manual uploads, and email.

- A context layer. Things people have done for years aren't written down, so an agent can't do them until we capture that context -- ranging from simple policies to complex decisions. This usually involves creating policy documents, shared agent memories, and skills.

- Evals and feedback loops. How you know an agent is doing a good job, and how it improves over time.

4) Why Ramp AI Solutions

We focus on finance because it’s the vertical we know deeply, have structural advantages, and are most differentiated:

- Data. 70k+ customers use our core product, over $200B in annual payments, years of vendor data, millions of transactions and bills monthly.

- Money-movement primitives and partnerships. Global money movement rails, partnerships with banks, Visa, Stripe, etc. You don’t want to vibecode international wires for bill payments.

- An intelligence layer on top: fraud detection from hundreds of millions of expenses, PO-to-invoice matching, state-of-the-art OCR, and fine-tuned models for accounting coding, spend routing, policy review, etc.

Unlike the labs, we’re not incentivized to sell tokens.

Ramp is an AI fiduciary and an impartial broker to deliver AI that is:
- model-agnostic -- we benchmark all the leading models (labs, open source) and fit the right one to each task
- and token-efficient by design

Our main incentive is business outcomes -- which is Ramp’s mission, to save our customers time and money.

I’m extremely bullish about our motion, and the broad industry growth of AI-native services.

If you're a finance leader trying to be more agent-native,

If you’re interested in joining our FDE team,

I’d love to talk 🙂

[Vaibhav Domkundwar @vaibhavbetter]

India AI Centers

Opendoor may have shutdown their entire 200+ India team and replaced them with a much smaller AI-pilled US team, but it may not really reflect what lies ahead.

We have said this before - global companies will build what we call "India AI Centers" as they go through the AI transformation of their business and need a talent base that can monitor and improve their AI workflows because unlike software, AI workflows will need constant monitoring, updating and more. Irrespective of the depth of the skill this work may need, it may be impossible for companies, at scale, to find such talent anywhere else except in India.

Would India's existing IT services workforce be directly relevant for this? NO - that is clear and we will go through some pain as programming (in this current form and its related tasks) is completely taking over by AI (the bulk of the work that Indian IT services does) and the spec for this new work of "helping their world run their AI systems" emerges.

India AI Centers will be built for this new work.

InCommon (https://t.co/hm7jHLPe1u) is already doing this - building India AI Centers for some phenomenal global AI companies. Nxtwave (https://t.co/NXtMX19yX1) and Airtribe (https://t.co/LF3HjpTAio) are building platforms for this new generation of talent that AI work will need. There are other new entrants in this space too beyond our portcos.

India AI Centers will be central to powering enterprise AI systems worldwide as AI transformation of the enterprise unfolds over the next two decades, and we move from software to intelligence and selling technology to selling outcomes. It will be a different world, and there will be short term pain to transition away from the IT services of the past 5 decades, because that is certainly dead.

The exact nature of work that will happen at India AI Centers will evolve rapidly from what is being done today to what will be needed tomorrow and onwards. So, building India AI Centers is not like building GCCs of the past - it requires a completely new mindset. Hard, not impossible.

Stay tuned as we do more in this area and find out if our contrarian view is smart or simply wrong :)

[Vivienne Su @CurtainSue]

This is a fantastic article that breaks down Loop Engineering in a way that’s accessible even for non-technical folks. It perfectly connects to how I already use features in Codex and Claude every day—automations, skills, worktrees, sub-agents, and persistent memory—and shows how Loop Engineering ties it all together into a smarter, more autonomous system.

Instead of constantly prompting the agents yourself, you design the ‘loop’ (a recursive goal + verification cycle) that handles discovery, execution, checking, and iteration on its own.

Addy Osmani explains the five core building blocks beautifully and why this shift—from manual prompting to system design—could define the next era of coding with AI.

[Nicolas Finet @nifinet]

https://t.co/TomJqBRVIv