Reetika Chaudhary

funmaxxing? trying 🤷🏻‍♀️ growth + marketing + tech + product + ai + startups

[Reetika Chaudhary @reetzcy]

@thebitsian Will they scream “ouch” or “delighted to raise 10 mil”

[Reetika Chaudhary @reetzcy]

never expected this punjabi dhaba in hsr to be like this. The food was ridiculously tasty, highly affordable, funniest owner, 90s truck highway songs playing, fresh tender chicken, crispy and fluffy kulcha.

[Reetika Chaudhary @reetzcy]

@praj_22 After your post I just came here

[Reetika Chaudhary @reetzcy]

@anmolm_ they need to just package chaas in a boujee way and voila! billion dollars

[Reetika Chaudhary @reetzcy]

@HealthcareAIGuy 100

[Reetika Chaudhary @reetzcy]

@elonmusk but how to use it? fable is available in claude interface which is so easy to use and the guides are innumerable on the internet. grok is so hidden and not easy to adopt in even normal workflows

[Reetika Chaudhary @reetzcy]

first saas to put ₹2000 instead of ₹1999 in pricing @claudeai

[Reetika Chaudhary @reetzcy]

My algo is pushing Nikesh Arora content so hard. Or is it just that his PR is too strong nowadays

[Reetika Chaudhary @reetzcy]

Reminded me of California in Bangalore

[Reetika Chaudhary @reetzcy]

@nakulusesgrok Excitiiiiiing

[Reetika Chaudhary @reetzcy]

I just discovered @vestaboard and it’s the most satisfying and fun way to show messages

[Nikesh Arora @nikesharora]

Summary:
I spent time trying to figure out this orchestration layer problem, can we design a multi model architecture in the long term. The more I dug in the more I understand that trying to build an abstracted layer is hard. As agentic activities increase and agent chaining and complex tasks get assigned to AI it will become harder to move between models.

There is a reasonable probability that 75% of the enterprises will build their implementation of the solution to their core problem around one model "stack".

Token price reduction by 90% is the solve and mobility between models from the same frontier lab!

Evals, harnesses, cache memory are the moats and I don't see models providing simple abstraction to those.

I know there are efforts to do this out there, the long term solve for orchestration if it works will need to be "Claude code" level of design genius.

Here's a chat with Fable @HamzaFodderwala had.

**Why abstraction looks easy.** Models are stateless — every API call is weights + a prompt assembled at runtime. Everything the model "knows" about you — memory, documents, history, tools — is injected into the context window by software outside the model. So in principle, all your state already lives outside the weights. The catch is what "state" includes.

**Layer 1 — Data (fully portable).** Enterprise documents, tickets, logs. Retrieved via RAG: text is chunked, embedded, stored in a vector database (Pinecone, pgvector), and relevant pieces are fetched into the prompt per query. The embedding model is separate from the LLM, so this layer is genuinely model-agnostic. Already solved.

**Layer 2 — Memory (portable in principle).** Systems like Mem0 and Zep sit between the app and the model: after each interaction they extract salient facts ("user prefers X"), store them as plain text, and inject the relevant ones into future prompts. Because the artifact is natural language, it reads into any model. Facts port.

**Layer 3 — Orchestration/routing (works, but only for shallow tasks).** Gateways like OpenRouter and LiteLLM normalize API differences and route each request to the cheapest capable model. This is the fungibility layer being furiously built. It genuinely works for one-shot, verifiable tasks — classification, extraction, summarization — which conveniently are the tasks where cheap models suffice anyway.

**Where it breaks — the non-portable state.** Four things stay behind when you switch:

- **The harness.** Prompts, tool schemas, and guardrails are tuned to one model's quirks. An agent must get every step right, so reliability compounds: a model that's 98% reliable per step completes a 50-step task about a third of the time; at 90% per step, it almost never finishes. Swapping models costs you a few points per step — the difference between an agent that works and one that doesn't.
- **The evals.** Swapping means re-testing everything and re-fixing every regression. The real switching cost isn't data migration — it's re-verification. Nobody has abstracted that.
- **Procedural memory.** Facts port; skills don't. Cached successful workflows and learned workarounds are conditional on the model that produced them.
- **Cache pricing.** Provider-specific, worth 75–90% of input costs on agentic workloads. Quiet lock-in.

**The labs' angle.** They offer hosted memory, hosted file stores, caching, fine-tuning — every one pulls state from your side onto theirs. The labs will crack memory first, but as lock-in, not portability. Nobody standardizes their own exit door. MCP is the partial exception: it standardizes tool and data access across models, but doesn't touch harness tuning or evals.

**Where 3P vendors fit.** Routers are thin-margin commodity plumbing; vector DBs and memory infra are real but small. The two structurally interesting positions: **eval platforms** (LangSmith, Braintrust) — since switching cost equals re-verification cost, whoever industrializes cross-model testing actually enables fungibility.

[Reetika Chaudhary @reetzcy]

Got diagnosed with gall bladder stone. Even tho common in women, the core reason is poor diet.
But I anyway avoid oily and calorific food.
The most terrible pain, hospitalised for days, surgery.
Now: Started to move my body - run, dance, stand.

[Loop @LoopHealthHQ]

We're giving away a free @UltrahumanHQ AIR.💍

Quote tweet this and tell us the story of what got you to start taking your health seriously: the habit you built, the thing you gave up or the day something clicked.

The most honest, moving story wins the ring.

To enter: Follow @LoopHealthHQ and quote tweet with your story.

🚨 Winner announcement on 13 July 2026.

Your story might be the reason someone else decides to start their healthcare journey today. Share it!💚

[Reetika Chaudhary @reetzcy]

Tried. Very refreshing. Pineapple was a bit less for me 🙈 but greattttt matcha

[krupali || chai is making matcha @chai_really]

@reetzcy @kunalvg hello try pineapple matcha at mitori matcha

in general it is just

pineapple juice and sugar syrup + oat milk + matcha topped on it

it blends together really well

[Reetika Chaudhary @reetzcy]

@tejeshwi_sharma NCR has massive Indian companies. Even though the number in Bangalore is more, the cap is lower and many foreign based startups

[Reetika Chaudhary @reetzcy]

@tereza_tizkova Yes. Happens if one has been going to many events

[Reetika Chaudhary @reetzcy]

The Coorg subReddit is just someone asking “is it raining there”, every day!!!

[Reetika Chaudhary @reetzcy]

@kunalvg Where’s this pineapple matcha

[Reetika Chaudhary @reetzcy]

@flenthomes Dislike button. No Bellandur, no marathhahalli