Raanan Bar-Cohen
RT @IainMacBets: No exaggeration, this guy might be the best interview in pro sports right now
04.7K03
Raanan Bar-Cohen✓
@raanan
Co-Founder @ResoluteVC: We lead super early seed rounds, including @ActiveFence @AppZen @AvenCard @greenhouse @HelloHeartApp @opendoor @paces_ai @ujetcx @vercel
5.3K Following5.2K Followers8.5K Posts
- Raanan Bar-CohenRT @davidmarcus: The thing I’m most grateful to Elon for is bringing back an age of wonder and excitement to humanity. That was jaw-dropping awesome! Screams and mouths wide open in the audience. Nuts.Tesla @TeslaRoadster01.2K05
- Raanan Bar-CohenRT @Spencerjakab: This @WSJ piece on the UK's runaway disability benefits is something else. Great color from @MaximColch & @davidluhnow070003
- Raanan Bar-CohenRT @tobi: Enter it into eurovisonMoritz Kremb @moritzkrembI told Claude to make a video about what it's like to be European One day later...06505
I told Claude to make a video about what it's like to be European One day later...3858165.7K1.2M- Great to see efforts like this. Here in CA we desperately need new fire suppressant tech. Firefighters here speak of being stuck in the 1950s in terms of tech.Peter H. Diamandis, MD @PeterDiamandisCongrats to @PalmerLuckey and Anduril for winning the Wildfire XPRIZE - for detecting and suppressing a wildfire within 10 minutes of ignition!009306
Congrats to @PalmerLuckey and Anduril for winning the Wildfire XPRIZE - for detecting and suppressing a wildfire within 10 minutes of ignition!
31479912.5K1.1M- Raanan Bar-CohenRT @rauchg: The data and the anecdata on Jev's adoption are shocking. Everyone is adopting it. I think it's a great product, but this is also downstream of the "AI is too expensive/slow" zeitgeist. People are eager to optimize and put AI in even more places!Vercel @vercelJev was adopted faster than any other model in AI Gateway history. In the first day, @typesafeai reached ~13% of teams, 2x the GPT-5.6 family and 6x Fable 5.1.04904
Vercel@vercelJev was adopted faster than any other model in AI Gateway history. In the first day, @typesafeai reached ~13% of teams, 2x the GPT-5.6 family and 6x Fable 5.1.
1451862.4K1M- Raanan Bar-CohenRT @dhh: Of course playing video games are great for cognitive development, reflex training, and strategic thinking. I trace my skills as a programmer, racer, and leader directly back to StarCraft, Quake, Unreal, and tens of thousands of hours of overall gameplay. START YOUNG, STAY YOUNG!Brandon Luu, MD @BrandonLuuMDFrequent video gamers performed on cognitive tests like non-gamers that were ~13.7 years younger035902
Frequent video gamers performed on cognitive tests like non-gamers that were ~13.7 years younger
6832.2K18.8K17.3M- Raanan Bar-CohenRT @NoamSCH: If you’re building or thinking about building at the frontier of security, safety, red teaming, evals, or anything that can make AI models safer and more secure, we want to hear from you. We’ll hire you, fund you, or partner with you - whatever it takes to help push the frontier forward. @alice_dot_io0102
- Raanan Bar-CohenRT @robmaurer: Cybercab is absolutely magical. Tesla has actually done it. A domestically mass-produced affordable autonomous electric vehicle is available to the general public. A car with no steering wheel generating revenue. It’s sad to come back to a city without Robotaxi after being in Austin. Like stepping back in time. The event was awesome. I know it was tough to follow online, but we all knew the story going in. This was a launch, not an unveiling. It felt like the city transformed immediately. Cybercabs were everywhere, you couldn’t take a trip without seeing another one. Outside of the ride experience itself, the scale of this launch is what I want to make sure people understand. On previous trips, Robotaxi felt like a test network (an amazing one). Not anymore. Suddenly it’s real. It’s hard not to laugh when you ride past a Waymo in a Cybercab. Good luck… As I’ve said before, Robotaxi is way smoother than Waymo. It feels natural. Supernatural, really. This holds true for Cybercab. Waymo feels like a computer. And it looks like one too. The production Cybercab is beautiful. It really is special. The color, the lines, the lighting, the doors. It looks great. While also being the most efficient car in history? Insane. I think it fits perfectly in the space between familiarity and the future. It doesn’t stick out, but it catches your attention. After exiting our first ride, there was a large group of 10-20 people. They were all talking about the Cybercab as we walked by. These doors are like gasoline on a word of mouth wildfire. Our rides were flawless. Better than a human driver. No anxious moments. Cybercab is very comfortable, even at 6’2. Leg room is particularly notable. It would be tough to find a car that has this much leg room for two passengers at the same time. No pedals, center console, or multiple rows of seating creates a lot of space and a very open feel. Robotaxi is unquestionably one of those products that will be way cheaper and way better than what existed before. I like those. No one is going to want to roll the dice on some random person and their car when the alternative is a private, safe, comfortable, clean, consistent, quiet, personalized Tesla with tons of room and a massive screen. It’s a premium experience on every level, and we get it cheaper thanks to technology. No noise or air pollution, either. I don’t have much to critique. Little refinements that I know will happen. Pick up and drop off were satisfactory, over time will become excellent. Grok integration is already impressive, and it’s clear that we’ll eventually be able to just give directions like “pull over around the corner up there.” You can already have Grok update your destination in Robotaxi, it pushes the update to the Robotaxi app on your phone for approval. I leave the event with more confidence in Tesla’s ability to scale, but also renewed patience. I have a greater appreciation for the intentionality of limitations (service area, times, fleet size) rather than seeing them simply as barriers that have yet to be overcome. I now believe the objective is to build slowly, safely, to critical mass. A tail-risk event is inevitable. The absolute last thing we need is some bad luck on a tail-risk event at an early stage before enough data exists to prove its rarity. That data is accumulating quickly and will become undeniable soon enough. Huge congrats to Tesla and Elon for this incredible milestone. Decades of work culminating in a life-saving, sustainable, affordable, economically exceptional product. It’s all pretty surreal, a testament to vision and determination. I love this company!01.5K03
- Don’t think we thank @ID_AA_Carmack and his team at id Software enough for accelerating AI. If not for games like Wolfenstein 3D, Doom, and Quake, we don’t get GPU development from folks like 3dfx, and later Nvidia. And we’d be 20+ years behind where we are today with AI.0010205
- Raanan Bar-CohenRT @TeslaSI: FSD Supervised v14.3.9 starting to roll out shortly This release includes a new active safety feature set: FSD Supervised can now activate on your behalf when an imminent collision is detected and Automatic Emergency Braking (AEB) may not be enough. It may also engage if we detect you’re heavily distracted or have accidentally disengaged FSD01.4K03
- Raanan Bar-CohenRT @alice_dot_io: There are infinite ways to break an AI. You can't defend against what you've never seen. Nearly a decade hunting abuse across the world's biggest platforms. That became Rabbit Hole. Today it protects 8 of the 10 leading AI labs. We've raised $140M, led by @ApaxDigital. AI is shaping the future. We're making it safer. @NoamSCH said it best 👇0203
- Raanan Bar-CohenRT @ADoricko: Rainmaker just produced ~19M gallons of water in Alaska via next-gen cloud seeding over 3 hours of operations. We are the first company to provably produce precipitation in Alaska. As promised, we’ve linked our white paper and relevant data. In the future, Rainmaker will protect and restore glaciers with man-made snowfall. Immediately, this demonstration shows how Rainmaker will add new water to the Colorado River and Great Salt Lake in the coming months. We turned around this analysis and white paper just a couple days after operating. There are many data sources I want to deepen our understanding of the atmosphere and increase our provable production; we’re building the instruments to collect that data in subsequent operations. But at Rainmaker, we won’t tout LOIs, simulations, or lab tests. What matters is physically measuring that you’ve affected the atmosphere; proving that we’ve produced water. We prefer sharing only the tech that has proven to make more water for farms, industries, and ecosystems in need. Expect a regular cadence of these from us as we build the best atmospheric science lab in the world. Feedback is welcome in the interim. Rainmaker Century.01.2K05
- Raanan Bar-CohenRT @matvelloso: Sarah Connor: "When they chase you, always run towards a wall. For some reason nobody trained them to stop. That's how we win in the future"el.cine @EHuangluchina’s AI robot just hit 14.5 m/s016K03
- Love this! A bit like the early web days of testing for response time and load times. Google favored the fastest loading sites, which was one of the early secrets that led to massive SEO optimization for certain sites. Same challenges and upside now with the age of agents.Guillermo Rauch @rauchgThis was wild to watch unfold. We ran 𝚒𝚜-𝚊𝚐𝚎𝚗𝚝𝚒𝚌 in a loop against until it got to 100/100. It made us close quite a few gaps. We worked hard to make sure the criteria is high quality and worth your time & tokens.003290
This was wild to watch unfold. We ran 𝚒𝚜-𝚊𝚐𝚎𝚗𝚝𝚒𝚌 in a loop against until it got to 100/100. It made us close quite a few gaps. We worked hard to make sure the criteria is high quality and worth your time & tokens.143742.2K460K- Such exciting stuff coming down the pike for Tesla with FSD. Gotta imagine that people should really try short-term leases (2 year) if possible, as hardware will continue to get upgraded. A bit like the early iPhone cycle.Sawyer Merritt @SawyerMerrittJPMorgan after meeting with Tesla recently in Fremont: "Tesla indicated it is intentionally holding back on adding Model Y units to the robotaxi fleet, expressing confidence in its ability to scale Cybercab in the near-term. On FSD V15, Tesla views this release as a step-change in performance, comparable to the leap from V13 to V14. The V15 upgrade encompasses seven core technologies, with ~40% of those currently being tested in the robotaxi fleet, where initial feedback has been encouraging. Tesla continues to focus on minimizing regression in core driving functions as it introduces new functionalities to the system, and FSD V15 is seen as the primary gateway to scaling unsupervised FSD. While the current AI/HW4 stack is capable of running V15 and supporting unsupervised FSD, Tesla’s AI4.5 compute system is designed to future-proof against rising compute (~10% higher FLOPS) and memory (~2x higher) demands as robotaxi models scale and context windows expand. Management also reiterated that the Cybercab is just the initial form factor, with additional vehicle types expected to follow as the platform evolves (citing the obovan demo from the 10/10 event as an example). Optimus is on track for SoP in the coming months with commercial sales expected as early as 2H27. The Gen 3 unveil will be timed closer to SoP to protect competitive advantage, while the scope of Gen 4 (in terms of capability, cost, and scalability) will be informed by Gen 3 field experience." JPMorgan toured Tesla's Fremont factory and met with the IR team, and maintained its $TSLA price target of $475.004346
JPMorgan after meeting with Tesla recently in Fremont: "Tesla indicated it is intentionally holding back on adding Model Y units to the robotaxi fleet, expressing confidence in its ability to scale Cybercab in the near-term. On FSD V15, Tesla views this release as a step-change in performance, comparable to the leap from V13 to V14. The V15 upgrade encompasses seven core technologies, with ~40% of those currently being tested in the robotaxi fleet, where initial feedback has been encouraging. Tesla continues to focus on minimizing regression in core driving functions as it introduces new functionalities to the system, and FSD V15 is seen as the primary gateway to scaling unsupervised FSD. While the current AI/HW4 stack is capable of running V15 and supporting unsupervised FSD, Tesla’s AI4.5 compute system is designed to future-proof against rising compute (~10% higher FLOPS) and memory (~2x higher) demands as robotaxi models scale and context windows expand. Management also reiterated that the Cybercab is just the initial form factor, with additional vehicle types expected to follow as the platform evolves (citing the obovan demo from the 10/10 event as an example). Optimus is on track for SoP in the coming months with commercial sales expected as early as 2H27. The Gen 3 unveil will be timed closer to SoP to protect competitive advantage, while the scope of Gen 4 (in terms of capability, cost, and scalability) will be informed by Gen 3 field experience." JPMorgan toured Tesla's Fremont factory and met with the IR team, and maintained its $TSLA price target of $475.
2466366.1K862K- Raanan Bar-CohenRT @garrytan: Direct-to-phone broadband from orbit, no ground towers required. Doing God's work.SpaceX @SpaceXDeployment of all three BlueBird satellites confirmed011403
- Incredible. Thank you @MaorShlomoMaor Shlomo @MaorShlomoTLDR: I’ve started a small fund from my Base44 money that’s focused on cancer research and breakthrough therapies. I’ve invested in 7 incredible companies in the past 4 months and plan to do more as my liquidity allows. Listing my investment thesis below I welcome anyone like me who has been blessed financially to join me and find ways to finally manage or even end this disease. Longer - I honestly believe cancer is humanity's worst enemy. ~40% will be diagnosed with it, and out of which a ~third won’t make it. And numbers are currently not getting dramatically better. For many it might sound like a statistic But for some of us who have been there ourselves or had to go through it with a person we love, it’s one of the worst things you can imagine. I had to go through it with my mom - who passed away 4 months ago and whom I miss and think about every single day since. This field needs a dramatic push. The current wave of technology and AI is doing a lot of good in the world, But cancer therapy is one of those things that are not moving nearly fast enough. And there’s no good reason for that - AI is creating so much opportunity right now. Here's my thesis on how things will play out: For drug development, the bottleneck will shift to 1. Manufacturing 2. Real world testing "In silico" is going to accelerate incredibly fast. AI is identifying new target proteins and designing new molecules, drastically shortening timelines. AI will create an abundance / inflation of ideas. It will help design molecules faster than anything historically possible. This is already happening. Play with Claude Science or Codex and you'll be surprised at how far you can get with just a few prompts. We need a better way to *manufacture and test* the 1000s of ideas coming from AI models spitting out potential drug candidates 1. Manufacturing: i.e. how fast can we synthesize a potential drug Not too far into the future, companies with manufacturing excellence will win over companies with the smartest scientists. Being able to synthesize a drug candidate fast - together with efficient methods to test efficacy (more on that below), basically means you get more shots at a target hence more chances at succeeding. This is the same exact lesson the software industry learned about the importance of fast iterations. Companies like Starget Pharma are leveraging AI heavily to iterate on drug candidates + figured out in-house manufacturing in a way that lets them iterate on drugs almost as fast as if they were a software company iterating on features. 2. Real world testing: i.e. how fast can we get a call on a drug's efficacy. We need better real-world indicators for drug efficacy than mouse / animal models. The success rate of animal models -> human clinical trials is less than 8%. Breaking down this bottleneck will change the entire industry. Developing an oncology drug and taking it through all clinical trials usually cost >> $ 100m Thus taking the wrong bet is devastating (but still, happens a lot). Giving drug development companies the ability to better understand the efficacy and mechanisms of their drugs will change a lot. Esp in the age of abundant drug candidates. Furthermore - and once those methods proved efficient - Joining forces with regulators to shorten (or even skip) some clinical trials altogether will cut years from the process and save many lives by bringing new drugs to market faster. CuResponse is pioneering functional testing - taking real cancer biopsies and showing drug efficacy with very high precision. Can be used for precision oncology as well as testing new drugs. Cellint is providing a fully automated cell-culture R&D platform for companies and researchers. ---- I’d love to see a future where there are “cloud services” for drug development companies. A company should eventually be able to submit a drug candidate programmatically and receive: 1. The synthesized drug. 2. Testing across cells, tissues, organoids, and tumoroids. 3. Structured results showing what worked, what failed, and why. 4. A recommended next iteration derived from the biological reactions. I’ve invested in all of the above companies and am searching for additional companies to bring this “cloud services for drug dev” vision to life. ---- Alongside it, I am also investing in direct therapeutic moonshots. I invested in Baccine, which is developing a bacteria-based cancer immunotherapy platform and deserves an entire separate post. I’m also a proud LP in Even One Ventures - @sytses ’s fund to fight cancer. Sid and Jacob Stern are an inspiration to me as I’m making my first steps in the field, and I bet their companies will make a massive impact. And 3 other incredible companies focused on how therapies are delivered - where I’ll elaborate in future posts. ---- I’m still obviously spending most of my time on Base44. Which is a plus for the companies - as we’re able to support them with tools, software, AI agents, etc. I’m looking for people to join me - invest side by side or help one of the companies. I don’t have a name for the fund yet, nor an email or a website I will get to it this weekend and will post it in the comments.305259
TLDR: I’ve started a small fund from my Base44 money that’s focused on cancer research and breakthrough therapies. I’ve invested in 7 incredible companies in the past 4 months and plan to do more as my liquidity allows. Listing my investment thesis below I welcome anyone like me who has been blessed financially to join me and find ways to finally manage or even end this disease. Longer - I honestly believe cancer is humanity's worst enemy. ~40% will be diagnosed with it, and out of which a ~third won’t make it. And numbers are currently not getting dramatically better. For many it might sound like a statistic But for some of us who have been there ourselves or had to go through it with a person we love, it’s one of the worst things you can imagine. I had to go through it with my mom - who passed away 4 months ago and whom I miss and think about every single day since. This field needs a dramatic push. The current wave of technology and AI is doing a lot of good in the world, But cancer therapy is one of those things that are not moving nearly fast enough. And there’s no good reason for that - AI is creating so much opportunity right now. Here's my thesis on how things will play out: For drug development, the bottleneck will shift to 1. Manufacturing 2. Real world testing "In silico" is going to accelerate incredibly fast. AI is identifying new target proteins and designing new molecules, drastically shortening timelines. AI will create an abundance / inflation of ideas. It will help design molecules faster than anything historically possible. This is already happening. Play with Claude Science or Codex and you'll be surprised at how far you can get with just a few prompts. We need a better way to *manufacture and test* the 1000s of ideas coming from AI models spitting out potential drug candidates 1. Manufacturing: i.e. how fast can we synthesize a potential drug Not too far into the future, companies with manufacturing excellence will win over companies with the smartest scientists. Being able to synthesize a drug candidate fast - together with efficient methods to test efficacy (more on that below), basically means you get more shots at a target hence more chances at succeeding. This is the same exact lesson the software industry learned about the importance of fast iterations. Companies like Starget Pharma are leveraging AI heavily to iterate on drug candidates + figured out in-house manufacturing in a way that lets them iterate on drugs almost as fast as if they were a software company iterating on features. 2. Real world testing: i.e. how fast can we get a call on a drug's efficacy. We need better real-world indicators for drug efficacy than mouse / animal models. The success rate of animal models -> human clinical trials is less than 8%. Breaking down this bottleneck will change the entire industry. Developing an oncology drug and taking it through all clinical trials usually cost >> $ 100m Thus taking the wrong bet is devastating (but still, happens a lot). Giving drug development companies the ability to better understand the efficacy and mechanisms of their drugs will change a lot. Esp in the age of abundant drug candidates. Furthermore - and once those methods proved efficient - Joining forces with regulators to shorten (or even skip) some clinical trials altogether will cut years from the process and save many lives by bringing new drugs to market faster. CuResponse is pioneering functional testing - taking real cancer biopsies and showing drug efficacy with very high precision. Can be used for precision oncology as well as testing new drugs. Cellint is providing a fully automated cell-culture R&D platform for companies and researchers. ---- I’d love to see a future where there are “cloud services” for drug development companies. A company should eventually be able to submit a drug candidate programmatically and receive: 1. The synthesized drug. 2. Testing across cells, tissues, organoids, and tumoroids. 3. Structured results showing what worked, what failed, and why. 4. A recommended next iteration derived from the biological reactions. I’ve invested in all of the above companies and am searching for additional companies to bring this “cloud services for drug dev” vision to life. ---- Alongside it, I am also investing in direct therapeutic moonshots. I invested in Baccine, which is developing a bacteria-based cancer immunotherapy platform and deserves an entire separate post. I’m also a proud LP in Even One Ventures - @sytses ’s fund to fight cancer. Sid and Jacob Stern are an inspiration to me as I’m making my first steps in the field, and I bet their companies will make a massive impact. And 3 other incredible companies focused on how therapies are delivered - where I’ll elaborate in future posts. ---- I’m still obviously spending most of my time on Base44. Which is a plus for the companies - as we’re able to support them with tools, software, AI agents, etc. I’m looking for people to join me - invest side by side or help one of the companies. I don’t have a name for the fund yet, nor an email or a website I will get to it this weekend and will post it in the comments.120871.5K238K- 100%.Justin Skycak @justinskycakYou cannot be creative at a high level unless you are robotic at a low level.001160
You cannot be creative at a high level unless you are robotic at a low level.
811.3K9.2K377K- Raanan Bar-CohenRT @justinskycak: How to compress a grade level’s worth of learning much, much shorter than a year: 1. Identify what the student already knows 2. Overlay that on a knowledge graph to construct their personal knowledge profile 3. Teach only new topics for which they've mastered the prerequisites, their "knowledge frontier" 4. Each lesson cycles through minimum effective doses of explicitly guided instruction & active practice problems 5. Enforce mastery relentlessly: if you can't consistently solve problems correctly, then you don't move on to more advanced material that depends on it. You continue on parallel learning paths and come back to the halted one later. 6. Review previously learned material using spaced repetition & frequent broad-coverage closed-book timed quizzes 7. Review old stuff by learning new stuff -- i.e., knock out as much review as possible by learning new material that exercises those review topics as subskills.Alex Smith @ninja_mathsFor anyone wondering how a third-grader can complete six years' worth of math in a single year AND score a 5 on the AP Calculus exam. This knowledge graph spans 3,000 math topics, from 4th grade to the university level, providing the perfect basis for mastery learning. Students can go as fast or far as they want! There are no restrictions whatsoever. The only requirement is that they must demonstrate mastery of each topic before moving on to the next. Kids are capable of incredible things when given that kind of freedom and support.010607
For anyone wondering how a third-grader can complete six years' worth of math in a single year AND score a 5 on the AP Calculus exam. This knowledge graph spans 3,000 math topics, from 4th grade to the university level, providing the perfect basis for mastery learning. Students can go as fast or far as they want! There are no restrictions whatsoever. The only requirement is that they must demonstrate mastery of each topic before moving on to the next. Kids are capable of incredible things when given that kind of freedom and support.
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