Anoop Mehendale

1K posts

Anoop Mehendale

Anoop Mehendale

@aprateem

health tech, data and enterprise AI entrepreneur with exits; healthcare exec experience across payer, provider, pharma

McLean, VA เข้าร่วม Nisan 2009
604 กำลังติดตาม233 ผู้ติดตาม
ทวีตที่ปักหมุด
Anoop Mehendale
Anoop Mehendale@aprateem·
Several years ago I was responsible for optimizing strategic spend (mainly tech) budget of ~$400M annually at a major enterprise. Most of the that money was spoken for to "keep the lights on" and maintaining systems that should not break. As I thought about the push to embed FDEs, it made me wonder what is missing to unlock the larger part of enterprise budgets, especially in businesses where reliability matters a lot. I think there is a need for a design surface that allows AI players to unlock this bigger prize. Here is my exploration into this 👇
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Anoop Mehendale
Anoop Mehendale@aprateem·
@signulll I feel like the big guys need to be focusing here- Apple, Google, OAI. The fact that they are struggling makes me feel there are many foundational problems or problems solvable only via massive vertical integration bets. What do you think?
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Kumar🇺🇸
Kumar🇺🇸@datarade·
I'll pay someone to turn opendesigner.io into a standalone desktop canva competitor with a local model that can do post card, website, flyer, and social media graphics outputs.
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Anoop Mehendale
Anoop Mehendale@aprateem·
In an age where building becomes easier (esp in tech), the only job is testing the market for what it is desparate for. I am not sure it is the same discovery mechanism as was followed before. It is perhaps closer to starting a bunch of small pre-revenue ventures with sell before you build approach, doing it at scale, and waiting for market response signals. What do you all think?
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Tim Ferriss
Tim Ferriss@tferriss·
Mike Maples Jr. (@m2jr) taught me the fundamentals of angel investing back in 2007/2008, and I've been revisiting my Kindle highlights of his book Pattern Breakers. One simple distinction from from the book worth revisiting often is this: are your customers interested or desperate? My new short post on the subject, including real-world examples: tim.blog/2026/06/01/des…
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Anoop Mehendale
Anoop Mehendale@aprateem·
I'm not even sure they can keep up the revenue growth rate for long after IPO. Noticed that Anthropic went from $10B->$19B->$30B->$45B and then only to $49B in May. They need to target enterprise budgets that are harder to scale into. Some of my thoughts on where they need to go: x.com/aprateem/statu…
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Nate
Nate@natebjones·
I think the more important shift is that Codex is turning into workflow packaging. Plugins, annotations, shareable apps, reusable instructions: that is the stack you build when the problem is no longer getting one good answer. The problem is getting a company to do repeatable work its own way. The next wave of AI software will win less on raw intelligence and more on how well it captures standards, tools, approvals, and context.
OpenAI@OpenAI

Building apps has never been easier. With Sites, Codex can turn your work, ideas, and plans into an interactive website or app your team can explore, use, and share with a URL. Rolling out to Business and Enterprise plans, before expanding more broadly.

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signüll
signüll@signulll·
once public, anthropic & openai will have to take far more calculated risks in both models & products. & everything will take a lot longer to do. talent shifts too. pre ipo you’re selling optionality on a moonshot but post ipo you’re selling liquid rsus with a perhaps capped multiple. that filters out the risk seeking slice, even if the median a player still shows up.
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Anoop Mehendale
Anoop Mehendale@aprateem·
Awesome! I once flew to a remote mountain town at the border of Washington and Idaho during the winter on a small chance I will secure a $250K/year contract from the local hospital. Almost died driving through a blizzard but got the deal :) The management team was super nice and appreciative of my willingness to meet them in person.
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George Munguia
George Munguia@jobsbygeorge·
Yesterday, I flew to Minnesota (took 7 hours), drove two hours to a town of 9,000 people, and delivered a bottle of whiskey to a prospect. We spent 2 days together touring their factories. Today, we got lunch / beers and he said he’s going to give us a shot. Meet in-person.
Sam Blond@samdblond

15/ 6. When possible, meet your prospective customers in person. Even if it’s not scalable. You’ll greatly increase the likelihood the deal closes by building a relationship with the buyer.

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Anoop Mehendale
Anoop Mehendale@aprateem·
Several years ago I was responsible for optimizing strategic spend (mainly tech) budget of ~$400M annually at a major enterprise. Most of the that money was spoken for to "keep the lights on" and maintaining systems that should not break. As I thought about the push to embed FDEs, it made me wonder what is missing to unlock the larger part of enterprise budgets, especially in businesses where reliability matters a lot. I think there is a need for a design surface that allows AI players to unlock this bigger prize. Here is my exploration into this 👇
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Hermes Agent Tips
Hermes Agent Tips@HermesAgentTips·
hermes desktop just dropped and it’s a HUGEE DEAL - native app for mac, windows, and linux no more terminal required - one memory, one agent identity across telegram, discord, slack, whatsapp, signal and more - visual skill timeline see exactly what your agent learned and when - built-in cron scheduler with natural language no cron syntax needed - isolated subagents for complex multi-task pipelines - MCP browser to install and toggle integrations visually - everything stays local… your keys, your memory, your data crossed 140k github stars in under 3 months and now the most used agent on openrouter and they just made it accessible to everyone this is the moment hermes goes mainstream @NousResearch
Nous Research@NousResearch

The next evolution of Hermes Agent is here! Introducing Hermes Desktop: everything you love about Hermes, now native on your machine. First demoed in Jensen's GTC keynote, it's now in public preview.

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Anoop Mehendale
Anoop Mehendale@aprateem·
@erikalee Good piece! I was at USC 2 weeks ago and could have tried to do IRL.
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Anoop Mehendale
Anoop Mehendale@aprateem·
@ivanburazin Honestly there are companies one should be willing to pay as a candidate if they give me a chance to work on a free trial and show them how valuable one can be. Because getting resume picked up for interviews is so hard these days without the connections.
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Ivan Burazin
Ivan Burazin@ivanburazin·
If you won't do a paid work trial, you've already told me everything. Either you can't do the job or you think you're above it. Both are a no.
Harry Stebbings@HarryStebbings

"We have everyone do work trials so people know what they’re getting into on both sides. We like candidates to do real work for 1 or several days, often over a weekend. When they see the office full on a weekend, they quickly learn that we’re not joking around." @nico_laqua What are your single biggest lessons on how to test the quality of candidates pre-hiring @awxjack @ryanjdaniels @ivanburazin @rronak_

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Anoop Mehendale
Anoop Mehendale@aprateem·
@vasuman Once you understand, how much would you replace and how much would you leave alone?
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vas
vas@vasuman·
Imagine an AI company that forward deploys into your enterprise to first understand how everything works, then architects what an agent solution looks like custom built for you, and only then builds the agents. Someone should uhh… someone should make a company that does that.
Tom Blomfield@t_blom

Imagine replacing 90% of your employees with a team of geniuses who have no idea how your company operates. Total chaos. Nothing works. That’s what AI feels like today. The missing piece is extracting all the domain knowledge from people’s heads and providing that as structured context to the models.

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Jeffrey Emanuel
Jeffrey Emanuel@doodlestein·
This is a really useful trick. LLMs are uniquely good at generating scores for attributes in a repeatable, consistent way. And if you want the scores to be robust, you can use multiple models and average each sub-score, with a penalty for when they diverge a lot between models.
Jeffrey Emanuel@doodlestein

@ryancarson You can define a multi-factor scoring system across various “soft” dimensions (e.g., an “elegance” score between 0 and 1,000) that gets rolled into a single weighted overall score and suddenly make just about any problem have a numerical loss factor that you can optimize over.

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Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp·
What is the most compelling example of a task in a non-verifiable domains where models really struggle? That might hint at lack of generalization from verifiable to non-verifiable domains.
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