Pramodh

Entrepreneur. Learner. Teacher. Have been a journalist. Love learning and teaching.

[Pramodh @pramodh]

@OriSilver AGI

[Pramodh @pramodh]

@shaurya_sinha7 Well done

[Zeb Evans @DJ_CURFEW]

Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why.

First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it.

Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands.

Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition.

I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively.

THE 100X ORGANIZATION
The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.

Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken.

The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems.

These roles will evolve. But waiting for that to happen naturally means falling behind now.

The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working.

THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS

— THE BUILDERS: 10X ENGINEERS
I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality.

Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment.

AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down.
Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed.
So who do you want orchestrating and reviewing code?

And how do you want your best engineers to spend their time?

If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code.

The new world is about enabling your 10x engineers to become 100x.

The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated.

I call this the great reckoning of AI coding, and every company will face this soon if not already.

More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well.

— THE BUILDERS: 10X PRODUCT MANAGERS
Product management and design roles are merging.

Designers that have customer focus, become more like product managers.

And product managers that have intuition for UX become more like designers.

The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results.

The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy.

Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on.

To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production.

Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck.
That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time.

— THE SYSTEM MANAGERS
Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp.

The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world.

You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is.

— THE FRONT-LINERS
In a world that will become saturated with AI communication, the human touch will matter more than anything to customers.

This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings.

One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers.

REWARDING 100X IMPACT
In a world where companies are able to do so much more with less, where does that excess money go?
In our case, much of the savings in this new operating model will flow directly back to those that enabled it.

We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them.

You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace.

Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.

THE FUTURE
Nearly every company will make changes like these. The ones that do it proactively will define what comes next.

The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago.

ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.

[Benny @hemzadev]

@OpenAI the world from 01/01/2026:

[Pramodh @pramodh]

@budapp Looks like claws are out everywhere...

[Pramodh @pramodh]

@AlfieJCarter MASTERCLASS

[Alfie Carter @AlfieJCarter]

Claude just replaced the $100K/year GTM Engineer telling your team how to use AI...

(most GTM engineers are still using it like a fancy search engine)

→ No more paying for generic AI training that doesn't apply to your actual stack
→ No more 10+ hours weekly figuring out which Claude layer to use for which task
→ No more one-off prompts that produce output you can't reuse or scale
→ No more sessions that start from scratch because nothing was saved or systematised
→ No more generic output because Claude has no idea who you are or what you're building

Just load the Masterclass → full Claude GTM infrastructure running across Code, Managed Agents, and Cowork.

Here's how it works:
→ GCAO Prompting Framework (forces specific actionable output every time instead of generic advice)
→ GTM Project Setup (loads your ICP, voice, files, and standards into every chat automatically)
→ Skill File System (slash commands that run qualification, enrichment, and personalisation on any list)
→ Co-work Prospect Engine (blank slate to 10 researched prospects with screenshots and cold emails in one session)
→ CRM and Gmail Integration (reads contact history and drafts 15 personalised follow-ups from actual notes)
→ Opus 4.7 Decision Framework (adaptive thinking, 3x image resolution, and cost management mapped to GTM tasks)

Built on the full Claude stack. Runs without consultants, agencies, or infrastructure teams. Zero re-briefing. Zero wasted sessions.

The difference isn't the model. It's the system built around it.

While everyone's opening a new Claude chat and typing from scratch, this turns the full stack into a repeatable GTM engine.

Want the complete Claude GTM Masterclass?

Like + comment "MASTERCLASS" + repost, and I'll DM it to you.

(must be following)

[Pramodh @pramodh]

@Shruti_0810 NEED

[Pramodh @pramodh]

@theo Building harness engineering practice from a practitioner implementing AI deployment at scale. It's like a framework:
https://t.co/LkNKYIhcqL

[Pramodh @pramodh]

@AlfieJCarter BIBLE

[Pramodh @pramodh]

@jordan_ross_8F Marketing

[Pramodh @pramodh]

@jordan_ross_8F OPENCLAW

[Pramodh @pramodh]

I am building a web app with @base44! The ability for the System to understand the requirement is the most amazing feature. Hat's off to the team at Base 44. A few things can be killer features: MCP integrations, Social Media integration with upload-post or composio.

[Pramodh @pramodh]

And that's the prep work for @thinkymachines of @miramurati who is working on bringing this determinism for LLMs. Thanks @karpathy . Have got early access to Thinking Labs playground. Will this deliver your Software 2.0? Let's hope. Time for hyperscalers to hustle...

[Andrej Karpathy @karpathy]

Sharing an interesting recent conversation on AI's impact on the economy.

AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are fundamentally about the automation of digital information processing.

If you were to forecast the impact of computing on the job market in ~1980s, the most predictive feature of a task/job you'd look at is to what extent the algorithm of it is fixed, i.e. are you just mechanically transforming information according to rote, easy to specify rules (e.g. typing, bookkeeping, human calculators, etc.)? Back then, this was the class of programs that the computing capability of that era allowed us to write (by hand, manually).

With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective. This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something. The environment has to be resettable (you can start a new attempt), efficient (a lot attempts can be made), and rewardable (there is some automated process to reward any specific attempt that was made).

The more a task/job is verifiable, the more amenable it is to automation in the new programming paradigm. If it is not verifiable, it has to fall out from neural net magic of generalization fingers crossed, or via weaker means like imitation. This is what's driving the "jagged" frontier of progress in LLMs. Tasks that are verifiable progress rapidly, including possibly beyond the ability of top experts (e.g. math, code, amount of time spent watching videos, anything that looks like puzzles with correct answers), while many others lag by comparison (creative, strategic, tasks that combine real-world knowledge, state, context and common sense).

Software 1.0 easily automates what you can specify.
Software 2.0 easily automates what you can verify.

[Deepinder Goyal @deepigoyal]

I’m not sharing this as the CEO of Eternal, but as a fellow human, curious enough to follow a strange thread. A thread I can’t keep with myself any longer.

It’s open-source, backed by science, and shared with you as part of our common quest for scientific progress on human longevity.

Newton gave us a word for it. Einstein said it bends spacetime. I am saying gravity shortens lifespan. 

Read on, and tell me what you think.

[Pramodh @pramodh]

@TheAINatives . Time to ramp thile AINatives experiences for learners. We saw this was coming and implemented and helped 3 batches of AI Cohorts with this thinking

[Cognizant @Cognizant]

Cognizant is partnering with @AnthropicAI to help enterprises move from AI pilots to scaled impact.
By deploying Claude across our platforms and delivery ecosystem, we're accelerating modernization, engineering and responsible AI adoption: https://t.co/3DDS02SGOd

[Pramodh @pramodh]

@MistralAI Great efforts to build something that experimenters and innovators should be using. No SDK, ADKs that AI Agents can use. Not even MCP. And have to fill a contact form. Time to understand the innovators and experimenters Mindset and ecosystem. Time for a GTM team of innovators.

[Pramodh @pramodh]

@samruddhi_mokal NANO

[Samruddhi Mokal @samruddhi_mokal]

Nano Banana + N8N = AI Creatives Factory

This AI system creates scroll-stopping visuals at scale using Google's newest image model.

No designers. No agencies. No $50K creative budgets.

Just endless professional-grade ads that look like top brands made them.

Here's how it works:
→ Upload your product catalog to Airtable
→ N8N automation scrapes product details and images
→ Nano Banana creates multiple creative angles for each product
→ System generates different backgrounds, styles, and compositions automatically
→ All variations get organized in Airtable with performance tracking ready

Each visual pennies to generate.
You own 100% of the assets forever.
Runs 24/7 without touching it.

While competitors spend hours in Photoshop or pay agencies thousands per month, you'll be cranking out unlimited variations automatically.

Perfect brand consistency across every creative.

Built 100% in N8N.

Want the complete workflow?
Comment "NANO" + RT + Like

I'll DM you the entire N8N template + Airtable setup
(Must be following so I can DM)

Skip this and keep paying designers $200 per ad variation.

[Pramodh @pramodh]

Oil marketing companies reporting profits in excess of Rs86,000 crore, central excise revenue touching Rs2.7 lakh crore, STATE VAT adding another Rs 2 lakh crore. Karnataka as a state has also got share of this VAT. This info was left out from the tweet.

[Priyank Kharge / ಪ್ರಿಯಾಂಕ್ ಖರ್ಗೆ @PriyankKharge]

Who really profits from Russian oil in India?

It’s not the common man. It’s Modi’s corporate friends, now the world’s biggest buyers of Russian crude who are locked in massive 10-year deals, ensuring unchecked profiteering.

- In FY24–25 alone, India exported petroleum products worth $60 billion using discounted Russian crude.

- Russian oil came 25–50% cheaper, but petrol prices stayed high for Indian consumers.

- Oil Companies posted ₹86,000 crore in profits, while the Centre raked in ₹2.7 lakh crore annually in fuel taxes.

- In April 2025 alone, excise was hiked by ₹2/litre, adding another ₹32,000 crore to government coffers.

If cheap Russian oil was truly in “India’s interest,” why didn’t Indian consumers benefit?

Instead, the common man paid the same high price and now faces fresh pain. Trump’s tariff threats could hit jobs, raise prices and destabilise the economy while oil companies and the Centre continue to profit.

The irony is brutal. The Modi government has deliberately blocked #AccheDin for the public so that its corporate cronies can cash in on the windfall.

https://t.co/Zz4bUCcHVj