SeedToScale

Actionable insights and connections curated by @Accel_India to accelerate your startup journey.

[SeedToScale @Seed2Scale]

A $200K show now costs $5K–$20K with AI video generation.

The budget is no longer access. It’s creativity and time.

@dementorSam of @dashversetech why AI will decentralize storytelling, on #DecodingAI with @anagh_prasad : https://t.co/FXeSnsHX92

[SeedToScale @Seed2Scale]

Microdrama = TikTok or Instagram Reels, serialized for an hour or more.

Watch the full episode of #DecodingAI with @anagh_prasad and @dementorSam : https://t.co/FXeSnsHX92

[SeedToScale @Seed2Scale]

You can't build the best AI filmmaking tool without seeing what audiences actually watch.

@dementorSam on why @dashversetech owns both production and distribution, on #DecodingAI with @anagh_prasad : https://t.co/FXeSnsHX92

[SeedToScale @Seed2Scale]

If a new model release can do your product's job on its own, you never had a moat.

The durable opportunity is in tasks where the model is almost good enough, and your system gets it over the line.

The Working Knowledge is SeedToScale's editorial property on ground-level thinking for AI. Its debut working paper, authored by @atemyipod, shows how to find that boundary for your tasks, build the harness to close the gap, and build the expert-labelled data that stays yours when the next model release moves the edge.

Read the full working paper → https://t.co/4icyCDGXWr

[SeedToScale @Seed2Scale]

Two teams can use the same frontier model and ship very different AI products. The difference is the harness around it.

Prompts, context, tools, memory, permissions and checks decide whether an uneven model is reliable in production.

The Working Knowledge is SeedToScale's editorial property on ground-level thinking for AI. Its debut working paper, authored by @atemyipod, breaks down how to engineer each part, and how to test every harness change before it ships.

Read the full working paper → https://t.co/4icyCDGXWr

[SeedToScale @Seed2Scale]

Introducing The Working Knowledge, a new technical publication from SeedToScale by Accel to help founders and technical teams make better decisions about the products they build.

It brings together AI research and lessons from practitioners in playbooks teams can put to work.

Our first, “How to Build a Vertical AI Product” by Tarun Raheja, explores how to evaluate models for your domain, build the context and tools around them, and make the product reliable in production.

With a framework and worked example, it also helps teams decide what to keep, simplify or remove as models improve.

Explore The Working Knowledge and download the playbook: https://t.co/4icyCDGXWr

[SeedToScale @Seed2Scale]

AI video generation is getting better. But making an entire film with it is a different problem. @frameo_ai is solving for it right here in Bengaluru.

How do you manage continuity across generations? Verify what an AI has made? Or orchestrate different models? And still leave room for human taste?

In this episode of #DecodingAI, @anagh_prasad talks to @dementorSam of @dashversetech about building @frameo_ai , an AI filmmaking agent ranked #1 in an independent benchmark by Physion Labs, and about the broader rise of AI in media and entertainment.

Watch the full episode: https://t.co/j3E4q7jUcH

[SeedToScale @Seed2Scale]

Model capability is shared. Customer-earned advantage isn't.

The Working Knowledge’s debut working paper, authored by @atemyipod, looks at how vertical AI companies accumulate durable value through proprietary data, workflow depth, customer deployments, trust in verified outcomes, and the assets built by doing the work with customers over time.

The full working paper drops soon. Get it 24 hours early through → https://t.co/QKVJK4mNvZ

[SeedToScale @Seed2Scale]

Treating your harness as finished is how last year's workarounds end up slowing down this year's model.

So, how do technical founders stop their harness from going stale?

That is one of the problems The Working Knowledge's debut working paper takes on, authored by @atemyipod

This working paper breaks down how to track what each part of your harness is for and test it against every new model.

It also breaks down why a good harness should get smaller as the models under it improve.

The full working paper drops soon. Get it 24 hours early through → https://t.co/QKVJK4mNvZ

[SeedToScale @Seed2Scale]

Your best AI training data may already be inside your product, in the mistakes human experts correct

The Working Knowledge’s debut working paper, authored by @atemyipod, shows how to turn production failures into a feedback loop, the data flywheel: instrument the product, use expert-guided error analysis to curate useful cases, turn them into evals, and use those evals to check whether each product change actually improves performance.

The full working paper drops soon. Get it 24 hours early through → https://t.co/QKVJK4mNvZ

[SeedToScale @Seed2Scale]

How much context should an AI product actually give its model?

The Working Knowledge’s debut working paper, “How to Build a Vertical AI Product”, authored by @atemyipod lays out a practical framework for making that decision: benchmark where performance starts to degrade, then use retrieval, compression, progressive disclosure or externalized state to manage context.

The full working paper drops soon. Get it 24 hours early through → https://t.co/QKVJK4mfGr

[SeedToScale @Seed2Scale]

Judging a model by its overall benchmark score is an easy way to make the wrong assumptions about which tasks can actually be automated.

So, how do technical founders work out what a model can reliably handle? That is the focus of The Working Knowledge's debut working paper, authored by Tarun Raheja.

The playbook provides a practical framework to score your specific tasks against expert answers and apply Harness Engineering to fix the gaps, showing you exactly when to use better prompting, external orchestration tools, or a human in the loop to make jagged models reliable in production.

The full working paper drops soon. Get it 24 hours early through → https://t.co/QKVJK4mNvZ

[SeedToScale @Seed2Scale]

Assuming a frontier AI model's intelligence scales evenly is the fastest way to break your AI product.

So, how do technical founders actually build reliable vertical AI products on top of unpredictable models?

That is the focus of “The Working Knowledge” debut working paper, authored by Tarun Raheja.

This working paper breaks down Harness Engineering: and the exact prompts, tools, and logic needed to make a jagged AI model reliable in production.

It also breaks down how to build a continuous data flywheel and durably retain your moat against the next major model release.

The full working paper drops soon. Get it 24 hours early through → https://t.co/QKVJK4mNvZ

[SeedToScale @Seed2Scale]

Watch Ep. 3 of Legendary Pivots, Why Meesho Walked Away As The Reselling Market Leader, Out Now: https://t.co/iS38DcAj50 (2/2)

[SeedToScale @Seed2Scale]

Your strategy can change. Your product can change. Your mission cannot.

@viditaatrey on the first principle he'd teach about pivots: a team needs one anchor point that holds still, because pivots take time and doubt compounds faster than results. (1/2)

[SeedToScale @Seed2Scale]

Download the Full Case Study here to know more: https://t.co/VYAQkdfrdN

[SeedToScale @Seed2Scale]

In 2021, @Meesho_Official walked away from its reseller business model their $300M Series E had been negotiated on.

Half the team was disheartened. Investors asked if it could be run as an experiment alongside the old model. It couldn't.

Here are five notes from its case study on why the pivot was still the right call:

1. Meesho never sold anything to shoppers. Women running small shops inside WhatsApp groups did. They showed products to their contacts and took the orders and Meesho got paid by the wholesaler.

2. Then phones got cheap to use, and Covid arrived. Shoppers stopped needing a middlewoman. Users who came to Meesho to resell fell from 70-75% to 35-40% between July and December 2020.

3. As Meesho left the model, Flipkart and Amazon walked into it. Flipkart launched Shopsy for small sellers in July 2021. Amazon bought a reselling startup the year after.

4. Most ecommerce companies take ₹10 to ₹30 out of every ₹100 a seller earns. Meesho took nothing. Its sellers were shifting sari falls and elastic bands under ₹200, where there was no margin to cut into.

5. Winning one customer cost the industry ₹1,000. It cost Meesho about ₹200, because resellers had spent years telling people it existed.

FY24: ₹45,650 crore in GMV, ₹7,615 crore in revenue, and ₹197 crore in free cash flow, which was a first for a horizontal ecommerce platform in India.

[SeedToScale @Seed2Scale]

Watch Ep. 3 of Legendary Pivots, Why Meesho Walked Away As The Reselling Market Leader, Out Now: https://t.co/smmLe1jzqx (2/2)

[SeedToScale @Seed2Scale]

Resellers were grateful. Consumers were not.

@viditaatrey on what @Meesho_Official underestimated when it left reselling in 2021: consumers arrive with expectations, and support, quality, and onboarding all had to be rebuilt to earn the trust that resellers gave for free. (1/2)