← Back to leaderboard
deedydas

Deedy

Verifiedactive
@deedydas

Partner at @MenloVentures. Investor: Anthropic, OpenRouter, Modal, Wispr, Pangram, Greptile, Inception, Goodfire. prior: Glean, Google Search. Cornell CS.

RosterAI Model News & ReleasesStartup & SaaS Builders
259,211 followers · 6,363 following · Last polled 59m ago
Perf. Score
84.8
Rank #13
Value Score
50.9
#130 of 273 priced
Median views
106K
p25 35K · trailing 7d
Followers
259K
+0.3% 7d
ER (impr.)
1.95%
eng ÷ impressions
ER (foll.)
1.42%
eng ÷ followers
Est. CPM
$32.87
post-rate basis
WoW views
-56.8%
▼ falling

Rates & value · est. from 106K median organic views

fairly priced
Quote tweet
—
no CPM
Postbasis
$3500
$32.87 / 1K views
Thread
—
no CPM
Retweet
—
no CPM
Value Score 50.9 · rank #130 of 273 priced$1755.27 / 1K engagementsrate age unknown (pre-audit import)

Trends

Follower growth
Median views / day
Engagement rate
View distribution · trailing-7d posts

Each blue dot is one organic post. The Performance Score ranks by the median (106K), not the mean (189K) — so a single viral post doesn't inflate a creator's reach. The p25 floor (35K) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
There is no faster city for flying than San Francisco. 9:10pm: Leave house 9:32pm: Arrive at SFO 9:40pm: Get to gate 9:43pm: Boarding starts 9:48pm: At seat I’ve never done a 38min house-seat time before.frozen10d ago2.0M4.0K505981503310.26%
“Cryptography is a subfield that’s extremely conspicuous by its absence from OpenAI’s list of 376 papers! But my sources tell me that the AI cos have now started, gingerly and discreetly, investigating whether their latest internal models can break important cryptographic protocols and primitives.” - Scott Aaronson, CS chair at UT Austinfrozen15d ago1.1M3.9K2228762884
0.46%
claude just generated this 2 minute video about the history of Google and it goes so goddamn hard https://t.co/tvlMAe0NWMfrozen26d ago722K2.8K233121771.3K0.62%
Meta Muse's retention is out, and it's almost as good as ChatGPT! It's higher than ChatGPT at D7 (>30%) and just lower at D14 (22%). Not quite in the elite D14 tier of WA (67%) YT (59%) Insta (54%) or TikTok/Snap (37%), though. Muse is one of the highest retaining AI products! https://t.co/Oe99a0EeOcfrozen11d ago720K1.6K1572051298710.42%
Gross incompetency from Indian certificate authority eMudhra. You had one job: generate a 384 bit private keys and instead you used: 4, 56, 66, 115, 155, 156, 230, and 237. This allows attackers to issue certificates for any websites. Embarrassing that it’s a family business run by largely computer illiterate people. Only 1 of the 3 have a remotely computer-related background and even they never actually worked a technical job in their life. If you look at the LinkedIn, their tech team is young, from tier 3 education and paid sort of bottom barrel. Imagine how many attacks you can design with AI given the existence of this genre of gross negligence.1d ago589K9.2K1.1K3441881.5K2.10%
The smartest Indians continue to come to build in the US for the simple reason that the economic incentives are far far better. Perplexity ($20B), Skild ($14B+), Baseten ($13B), Together ($8B), Glean ($8B), Modal ($5B), Rubrik ($25B), Harness ($5B), Blacksmith, Parallel, Greptile, CodeRabbit, Composio, Distyl ($2B), Resolve, Luma, Cartesia, Wispr, dMatrix, Arize and many more - all started by at least one Indian-born founder. The US is a far easier to raise capital in with far less dilution. Most Indian companies are switching where they're domiciled to get access to the US capital markets. Every single Indian venture firm is trying very hard to invest in the US or risk not participating in the AI race (ironically, the management fees of venture firms are so high in India relatively that this is the one group that net of carry actually has incentive to be in India because they take home well north of a living wage whether or not funds perform do) While Zomato ($35B now, $8B at IPO) might be the biggest outcome in Indian venture, Cursor ($60B), Reflection (potentially $20B+), World Labs ($8B), OpenRouter ($8B), HuggingFace ($12B) and of course the $1T+ big labs quickly make those outcomes seem small and happen on a much smaller time scale, with much higher IRR. Engineers at these startups regularly get paid $1M/yr, and make $10M+ on exit. Even in BigTech, it is trivial to pull up levels fyi and see that if you're L6/L7+ with ~10 years of experience, you're making $1M/yr there too. And of course, we all know the slew of Indian CEOs of companies here, not to mention a huge bench of lesser known executives across the board in public and private companies. I say all this because every time there is a slight immigration hiccup in the US, I hear the online pleas to "build in India", I struggle to understand why most engineers, academics or founders would do that net of desire to be close to family, failure to obtain visa and devout patriotism. This is not a superiority thing or a US v India thing. The economic incentives at least for technologists to being in the US outweigh India by at least an order of magnitude.frozen11d ago504K4.3K3402351161.5K1.29%
People say this all the time, completely ignoring that China, Singapore, Korea, Israel, Malaysia, etc also became independent around then but are far more developed. When you point this out, they say “but that’s different.” The reality is this is decades of atrocious governance.frozen27d ago409K4.9K315267584791.47%
The economics of a Neolab. A neolab is loosely defined as a startup of AI researchers who raises a lot of money pre-production to be able to finance GPU compute to take on a large AI problem. To buy 1000 GB300s or ~14 NVL72 racks will set you back $125-150M for 3yrs with 15-30% upfront. That’s about ~2-2.5MW. Thats about enough to do 10^25 flops a quarter and get to a GPT-4 level model which is 1-2 OOMs off frontier for pretraining. If you post-train on a great open source model, you have a better chance of getting to frontier. The risks are a) you need to spend millions on RL environments too and b) being lapped by another model release while being tied to a base model. For this to payback, you need to give your customers a better and ideally cheaper inference service than a base model and serve them for long enough to recoup your large investment. Even at 50% margin on inference, to recoup $10M in training means serving ~10T tokens (!) if you price like Fable / Astra given a standard cache read / input / output split ($2/M blended). And you have to justify being better than a release like Opus 5.5 which is even cheaper. Often, you end up charging your customers a huge premium in terms of platform fees and compute fees on top of pure inference. Meanwhile, every hour you’re not utilizing your GPUs you are burning money so you typically resell this compute back to a broker or run inference for open models / resell spot instances. At below a ~60% utilization on spot, you will still lose money. Add to that insane cost of talent. So what can you do with the compute? - Not play the model game at all. - Play an entirely different model game (Jev, World Labs) that if big labs played, would either a) cannibalize their business or b) be incrementally not significant revenue c) would cause too much distraction from the main main thing - Acquire a proprietary data set (Peridodic Labs) in enough volume in a domain of usefulness to eclipse frontier quality. Often happens in robotics, biology, chemistry. If you do overcome the challenge of building a model that is useful and well priced beyond big labs models, given the huge price of compute, you still need to play in an area where the revenue / compute ratio is signficant and market demand is large enough to payback your compute spend. It is a difficult game.frozen26d ago369K2.2K156177582.3K1.32%
Met a kid today who taught himself AI with LLMs at 14 while barely speaking English, worked a 1yr long internship in a top AI company, flew across the world to attend a top 10 US undergrad, dropped out in 2 weeks, and has 5 ~$1M+ job offers from the top neolabs in SF at age 17. Traditional education is so dead. The young kids today are just built different.frozen11d ago368K5.7K338249552.1K2.29%
The US DoL has suspended new and pending PERMs from prominent WITCH Indian IT companies (Wipro, Infosys, TCS, Cognizant, HCL, Capgemini) and Microsoft and Adobe. This means their H-1B employees will be subject to a 6yr cap with no extensions and not have a green card pathway.frozen14d ago364K3.1K145151424351.08%
Opus 5.5 is incredible at instructional video generation. I made this launch video for a inference startup in 1min for ~$2. Videos like these used to take weeks if not months and a lot of coordination with agencies and 1000x the costs. Humans broadly prefer video to text. This changes the substrate of communication. These videos actually help communicate technical ideas in seconds (photorealistic video gen like Seedance is not very useful here). - changes how often marketing should be talking about products and launches - change how sales people can talk about technical products to their customers - allow technical people to easily explain concepts internally without long docs And thats just scratching the surface within startups. Prompt: “make a modern slick and punchy video for a modern startup that works on inference”frozen29d ago348K3.3K152208334.7K2.40%
You can now generate an entire 3blue1brown style video from any research paper with Opus 5.5. Here’s a 8min video summary of “Regularized Recursive Self Improvement of Agent Harnesses”. The 90%ile educational YouTuber is fully automated. https://t.co/HTOOq1JsnPfrozen28d ago332K5.6K398176685.4K3.50%
Google is an incredible company that I’ve always rooted for that trips over its own bureaucracy in frenetic times. They ironically do a middling job at their first priority, but crush it at their second or third. We’ve seen this with Search, Google+, Assistant, Cloud and now even AI. Why? The internet would be unrecognizable without Google. This is a company that has Search, Chrome, Android, Workspace (Docs/Sheets), Drive, Play Store, YouTube, Gmail, Maps, Photos, Translate, Gemini, Calendar, Meet, Chromebooks, Waymo: some of the most widespread and most undermonetized products in the history of the world. It is inconceivable for most of us to live life without software Google has made available for $0. And truly, having worked there, this is a company that continuously prioritizes their users over profits. Even as a casual reader, it might be trivial for you to imagine 100s of ways to monetize all of Google’s products. But they usually don’t. They don’t just launch experiments because it’s high engagement, but only when it’s actually good. So how does a company like this falter? My observation has been that these frenetic eras either attract or condone the worst behavior out of execs that trickle down to the rest of the company. The most “ambitious” L3-L7 people who feel stuck finally see a company-wide priority and their eyes light up with the gleaming prospect of a promotion. A lot of people in big orgs’ entire sense of self esteem is wrapped around their level. They will do a lot for an N+1. “Did you know Sergey is personally working on this?” “Sundar referred to my project in the all hands” In turn, everything in these orgs become a knife fight for getting the most “high impact” projects, fighting for credit, flagrantly hiding concerns around juiced metrics. More work goes into a promo packet than the actual project. Goodhart’s Law kicks in and the metrics measured for a promotion are abused beyond measure. On the other hand, second and third priority things work brilliantly. It attracts people with genuine interest, sincerity who are willing to play longer term games. Many products have grown and thrived when left alone. When there is immense pressure for a number to go up, it eventually does but often at the cost of product quality (the small product polish things don’t get me a promo), core innovation (why take on a high risk bet if I can get promoted for copying oai/ant) and cultural cohesion (a lot of bad blood amongst people in the org, tons of reorgs). There is a certain type of individual that thrives in this environment, and they are typically not very likable nor “Googley”. This is why an incredible number of people leave or allow jesus to take the reigns as they cash in the bag. I lament that every time this happens, we, the billion users, lose out on yet another beautiful Google product.frozen18d ago292K1.7K79116317100.91%
This is serious. I just saw with my very own eyes that even non-technical people can exploit CVEs like PHP2Shell using certain AI models and I am actually frightened. Once you figure it out, it is downright trivial to do. The digital infrastructure of the world is at risk.12h ago277K2.4K111201585561.19%
I made a short 5 min documentary on the greatest moments of Indian history from 1947 to today https://t.co/YO0qgheWaWfrozen11d ago264K4.1K598106371.9K2.56%
my favorite Paul Graham essay “how to do great work” in under 200 seconds of video (thanks opus) https://t.co/C1GCCfjgGOfrozen25d ago255K4.5K460116525.6K4.20%
I’ve had 3 diff people tell me they are 100% sure this “fastest to $1B annualized revenue” is 3 diff companies. Very likely more than 1 data co has recently hit that mark, many of which are on Pavlov’s List. Many more will get there in the next 6mos. Total data spend is >$15B. https://t.co/rIdpwcJYhHfrozen15d ago226K9187153141.4K1.09%
This is one of the most important results in AI, period. TLDR: neural networks are not tangled knots (polysemanticity is wrong). You can study their own neurons directly, without first "unfolding" them with a second model. 90% of neurons in Qwen-32B have one job. Nick Cammarata started OpenAI’s interpretability team and is at Goodfire now. He’s been studying how LLMs work for a decade. The stakes for understanding and aligning model behavior are the highest it’s ever been. Models have achieved superhuman results in math, can break production software systems, and changes the nature of white collar work. This work may finally allow us to intentionally design LLMs and shape how they behave. Its a long, phenomenal read.frozen7d ago192K2.0K1105981.5K1.94%
Zuck learning the bitter lesson:frozen10d ago171K2.3K425062811.55%
LLMs are either not capable or not affordable for visual reasoning tasks at scale. The Elorian team - one of Jeff Dean’s best reports who led Gemini, an Apple LLM lead, xAI lead, and a Harvard professor - just launched an Astra-level visual reasoning model, Glimpse, for 17x cheaper. Watch it break down the flying Tesla video physics here. Extremely critical for video inputs in manufacturing, robotics, gaming, content moderation and more.frozen6d ago164K2.5K160134211.9K2.89%
I've spend 10+ hrs perfecting how to get the best out of Opus 5.5 for video generation workflow: - Use it with Claude Code, not the app. - Use OpenRouter API to access all the other models (image gen, video gen, audio gen) with one key - Use Gemini 3.8 TTS and make it create a skill from the API to put emotion into the voice. - Use Manim, Hyperframes, Motion Canvas for motion graphics for HTML web grounded video - Use GPT 2.5 Image Sunburst for keyframes and Veo 3.1 / Seedance 2.5 for generation. Seedance does better with motion shots. Ensure you have reference images for consistency and generate an animatic before full video. - Add a script writing planning skill. Use OpenTimelineIO to assemble. - Explicitly tell it to avoid Claudisms like short punchy sentences and a lot of numbers. “Narrate like a university professor.” - Allow it to use yt-dlp with search to inject clips from real videos (creative commons) in composition - Use ElevenLabs for music gen in the background - Add a caption skill which uses any decent ASR model to get word-level timed subtitles on the final video - Add a critic skill which takes screenshots and uses transcription to validation the audio and video quality In the prompt, specify a) what you want b) aspect ratio c) length d) style preferences. ffmpeg handles most of the actual video manipulation tasks. Here's an example of a 4 minute Neocloud business explainer:frozen23d ago155K2.1K143161234.3K4.35%
As cost of coding has plummeted, long builds (CI) has become a bottleneck for many software cos. Three of the biggest startups solving this: 1. Blacksmith (@useblacksmith) 2. Namespace (@namespacelabs) 3. Depot (@depotdev)frozen25d ago142K1.0K5087181.1K1.60%
The last 18mos has seen an insane nearly ~$200B of AI startup exits via M&A. The median age is 4yrs old. It is absolutely possible to return a multibillion dollar venture fund with phenomenal IRR, even if you aren’t in big AI! This is one of the hottest liquidity markets ever. https://t.co/fUxXnYrZSofrozen9d ago122K9699393297311.57%
The serious answer to how Shazam worked is it took the peaks of a spectrogram of short clips of every song, find the peaks from the highest amplitude bits, hash it with the value being (time stamp, track id) and then the actual recognition is a hashtable lookup. We did this for a college CS project. The original paper is fantastic:frozen17d ago116K3.8K35962201.5K4.91%
every single open source repo should have an explainer video like this 7min one for SQLite 1. explains what the repo does and why its useful 2. shows a high level map of the code (beautiful graphic!) 3. life of a query through the codebase 4. the core abstractions in the repo 5. a real trace of execution and what happens, with a bit on join-order query planning and yes, this was all generated by opus 5.5 w/ gemini 3.8 tts. i sound like i'm on repeat, but im in shock at just how coherent and capable this model is. everything truly is code.frozen25d ago100K1.8K12789252.0K4.04%
OpenAI’s math release is, in the words of Opus, “the single most consequential mathematical release ever” * LLMs have how made substantial progress on 4 of 7 Millenium Prize problems: Navier-Stokes (claimed), Riemann, Hodge and Birch-Swinnerton-Dyer. Poincaré was solved in 2003. The two left are P v NP and Yang Mills. *conditional on verification On average, each OpenAI result used only 3hrs of thinking compute on their new unreleased models. Two years ago, models said 9.11 > 9.9. Today, we have results that the smartest human minds have not been able to achieve in their entire lives. It is clear that data, compute and algorithms scale. Every model generation (3mos) has made substantial intelligence progress. It also becomes incredibly hard to not believe that all knowledge work will change monumentally over time. The things AI can not do in the foreseeable future are very likely context-bound (don’t have access to the right information) than intelligence bound. Some might argue they are also creativity-bound or judgement-bound (what should I work on), although it can be argued that future generations could solve for this (given, say, the advancement in research taste for models over time). AGI is defined as surpassing human capabilities on virtually all cognitive tasks. By most interpretations of that definition, we are there. The domains humans are still better than AI, such as, robotics / physical world control (data-bound?), natural science research (data-bound), some creative domains like writing, movies, music (creativity-bound), super long tasks (context-bound), choosing problems to solve (creativity-bound) and maybe human relationships management (meat-proxy bound?). In many ways, we have achieved AGI.frozen15d ago95K8906279161961.31%
It's still hard to fathom that Big 4 capital expenditure buildout for AI is 2x the peak of Telecom a quarter of a century ago at 2.4% of GDP. Reminder that this is the biggest buildout in infrastructure in America, eclipsing even railroads. https://t.co/hRuNrvwpZMfrozen27d ago93K36333463840.57%
We’re at a point in late stage AI model capitalism where you know the frontier has to win in a bunch of benchmarks to launch. These numbers mean nothing. The thing to trust is the price. If they price it high, it’s a good model. If they don’t, it’s benchmaxxed.frozen22d ago81K723239616641.13%
“Translate this IKEA assembly manual into a 3D narrated instructional video” with Opus 5.5. Overnight, Opus just became the most actually useful video model, via code. https://t.co/Q2kNygBPW5frozen28d ago75K1.0K7662146022.40%
America: a 250 year tribute. i made this 5 minute documentary completely orchestrated by opus with a montage of the remastered versions of the most iconic moments of US history https://t.co/KG4W5t9q68frozen24d ago73K279184081600.69%
If you thought this was a bearish view on neolabs, let me write the bull case. 1. Compute is a great case of Helmer's cornered resource. There is a window of time where you can acquire a lot of compute and capital. In the future, both funding might dry out or compute prices might continue to rise. It could get very difficult to compete. 2. Big labs will suffer from innovator's dilemma. They're unlikely to: a) deviate too much from their cash cow, coding b) ship anything that cannibalizes revenue c) go after opportunities that incrementally look too small (<$1B) 3. Empirically, many neolabs are working and generating significant revenue. They're just not being loud about it. Admittedly, it's still very much early days. 4. A lot of talent sees more financial upside in neolabs than big labs. Whether the companies show signs of life or fail entirely, there are at least 5-6 large promising acquirers. All startups are difficult and the point of the original post was to illustrate specifically the difficulties of running businesses with massive upfront compute purchases, not say it's impossible.frozen25d ago69K351164543701.14%
Menlo has tried to rebuild from the ground up how we think about investing and we are fortunate to be lead investors in 5 of the top 15 consumer AI apps by monthly revenue despite our low volume strategy. Thanks for the shout, Shaun! Have a ton of admiration for what you have done at Sequoia. Too many people know you for your firebrand tweets and too few know your excellent investments and that you have a PhD in physics from Caltech (and it’s fun to have your cofounder be a partner!)frozen17d ago63K474254421971.18%
Higgsfield just entered the ranks of iconic startups that have reached $1B in revenue run rate. Alex Mashrabov (@alexmashrabov) is an absolute force of nature and we’re so fortunate at Menlo to have led the seed round.frozen28d ago52K29112233800.79%
If you’re a VC dunking on this, you better have a grand total of one investment. Otherwise, you are, definitionally, buying yourself options.frozen17d ago51K1261265460.40%
When I joined venture 2 years ago, everyone told me I was foolish to pick Menlo Ventures. Today, research from the best Stanford professor of venture capital ranks Menlo as #3 in 2026, and #1 for <$25B in AUM, with 50 unicorns and only ~20 investors. These rankings rewards being early, leading, valuation, exits and recency. Rankings are just rankings, but it’s extremely rare for VC firms to change reputations so quickly. It’s a small milestone to celebrate. Menlo has spent the last 5 years rebuilding the team with people who have built startups and long time investors. We built a culture around sticking to first principles while staying true to our roots of low volume high conviction bets. In the last few years, we have been fortunate to partner with Anthropic, OpenRouter, Lovable, Suno, Fireworks, Gimlet, Higgsfield, Wispr, Modal, Legora, OpenEvidence and they are really the stars of the show. And we’ve missed innumerably more. However, in venture, you’re only as good as your next deal.2d ago49K488173561501.41%
OpenAI tells investors their annualized revenue is $50B for end of September, up from $30B in July, short of the $70B previously reported. Anthropic was at $65B annualized end of July. https://t.co/EbFANrMl95frozen14d ago39K28718514431.03%
Modal is one of the only companies in history to grow 10x in <1 year on a 9 figure run rate base to $1B! It’s a privelege to continue partnering with them: it was the #1 investment for me when I joined venture and has become a key part of the way most companies scale, including Jev. Erik and Akshat are also some of the nicest, most thoughtful founders and now join the very exclusive club of IOI gold medalists x decacorn founders.frozen8d ago32K37811231981.58%
The velocity of code changes for engineers this year has 10x’d, quickly moving the bottleneck to code review and testing. Greptile solves this for the most technically challenging codebases like Nvidia (core CUDA code is reviewed on Greptile), Coinbase, Datadog, OpenClaw and 22,000 more customers. Many people think the model layer will eat code validation, but I don’t think it will. Code review within organizations is a more context-bound task than an intelligence-bound. The most intelligent AI can be wrong because it breaks an assumption in one API endpoint another service relies on. Large codebases can quickly accumulate a lot of technical debt your coding harness won’t reliably remember every time. Greptile does. Instead of having a frontier model harness review your code which can quickly cost $20+/review, Greptile does a much better job for much cheaper, leveraging the smartest models when needed but the GLM/Kimi/DeepSeeks when needed to. And it also runs, tests and takes a video of what your change did. Over time, organizations treat a 5/5 rated change by Greptile as a guarantee this won’t break anything in prod. And it shows. Their growth has been off the charts. Incredibly fortunate to partner with this a company. Fun fact: Elon once posted Greptile's growth graph with "it's so over" pointing to 2024 to show AI's acceleration.frozen3d ago30K239224411001.35%
I used Opus to convert OR's public token stats into $$ stats. A whopping ~$1.5B annualized run rate (week x 52) gets processed on OpenRouter, putting it at ~1% by dollars of total inference. This week, Anthropic took 36% of spend, OpenAI 18% and open-weight models took 26%! https://t.co/o8jtBReDxCfrozen12d ago26K16513585541.14%
Inception just dropped Mercury 3, a model 4-8x faster than Luna / Haiku for the same price and benchmark performance. In real world use, OpenRouter shows that it’s effectively 2-4x faster and 2-4x cheaper effective price. The time for diffusion language models is finally here! https://t.co/Cwt73DDG2t3d ago24K336246801132.26%