Ash Perger

@RainbowBodied A Buddhist monk once told me that I should imagine the difference in perception between a normal person and advanced practitioner as follows: the normal person finds it hard to even trace a single raindrop in rainfall, the advanced practitioner can trace every drop.

The development of your consciousness is directly correlated with your rate of perception, slowing down paradoxically is how to get from 30 to 60 FPS. You can think of life like a video game, the wider the context that needs to be loaded the slower the game runs - when your thoughts are scattered and you’re jumping from one thing to the next you are forcing your computer (mind) to load lots of context, thus creating lag or latency. Conversely slowing down creates presence, and the brain focuses in on a more limited set of informational input. When you have less input the computer (mind) can load the information in front of it in much greater detail. Information transfer occurs faster, and your vision goes from HD to 4k. That’s what “focus” actually is, the elimination of scattered thinking, and the presence of straight forward, concentrated, directional thinking. If you can maintain that focus throughout your day you’ll enter into what is classically referred to as the “flow state” or the unbroken chain of concentration on what is actually present in front of you. Step 1 is always to slow down, take a big deep breath in, and another big deep breath out - you got this.

@LoganTGott 30

We took a tech founder from 1 LinkedIn post to $250k in pipeline in 30 days. This is not clickbait. Starting point: 1 post ever, 0 leads, in stealth Impressions added in 30 days: 690,000 New followers: 700 Biggest single post: 150,000 impressions Intro booked: a $1B tech company Pipeline generated: $250,000 I wrote up exactly how we did it so you can do the same. Comment "30" and I'll send it your way in a few minutes.

@p_millerd @david__booth @MiguelMayher would join anywhere in Asia

@david__booth @MiguelMayher might have recs - could be a resort style place in da nang like i know hes been in the past

A few weeks ago we packed 15 families - 29 parents & their collective 37 kids - into a giant 18-bedroom house in Tahoe, and hosted a super fun "un-conference" style retreat/gathering. very casual... social thing. run with/for friends. But thanks to an epic support crew of nannies and @Alphaschool guides, all were able to participate- most facilitate a session of some kind.. some personal, some professional, some family subjects, e.g - "building a company with your spouse" ft @clairevo & @elawless, @hsambhi & @rosie_chopra + others - @jessegenet on running her homeschool + household with local AI agents (including live appearances from the kids) -@mbateman and @nateliason on "how to start a school" - @adamgries on longevity & AI, how to live forever - @Gena_I_Gorlin on the psychology of ambition - plus "building connection" for busy married couples. - - @immad's philosophical takes on "what is money" - and raising kids to have positive relationships with it. - - @ketau on “when agents spend more than humans” / future biz models + ad supported AI - @ellenvoramd and @vimalspot on "Modern Spirituality" - building spiritual families, deep dive from Ellen on "the Anatomy of Anxiety" - also - @TomHacohen on Privacy, security, AI & and the internet; @Alicebentinck on how to spot talent, @petergyang on building distribution; life on the road and cultural immersion with @rieglobe, @abatalion & others. This was round two — first was back in 2023. Probably not going to wait three years to do it again. Maybe east coast later this year? 👀

@signulll @drmichaellevin's podcast is actual alpha: thoughtforms-life.aipodcast.ing

most podcasts now have become unwatchable. i rarely ever have the desire to listen to any of them. there is virtually zero alpha & very few if any original thoughts behind them. most often it’s just twitter takes kinda laundered to sound a bit more unique than otherwise. also they’re so scripted & highly produced that it just doesn’t even make any sense often. does anyone really need 94 minutes of throat clearing to hear the same three points you read on x last week from some anon? youtube has a nice summary feature that works well if you’re curious about the content. there is only one exception here & that is ben thompson who consistently produces original thoughts over & over again for years now. his cadence & thought structure both written & in conversation is quite beautiful through & through.

@dwr dayflow.so cc @jerryliu

Is there a cost effective and straightforward way to: 1. Record video of everything I do on my Mac for a week 2. Feed into an AI 3. Have the AI recommend concrete things to automate? Coach me on stuff I'm inefficient at?

@virgilerietsch @virgilerietsch gave you a follow, dm please to discuss

Personal update: I'm looking for a new job! I'm a passionate builder that just loves solving complex problems. I've built many fullstack apps over my 6 years careeer. With AI everything changed : I leverage it to solve even harder problems and build more ambitious apps. Today I'm looking to join a company with the same mindset : - who cares deeply about UX and UI - who is using or willing to use AI to have an even bigger positive impact in the world If that's you, please reach out ! P.S : I've also built many AI agents, workflows for content creation and mobile apps !

@gabriel1 @elonmusk three main things I learned: 1. drill down on work they fully owned vs. part of team effort 2. ask questions about how their biggest failures and screen for honest ownership of personal mistakes (instead of blame) 3. screen for agency / proactive cross-functional problem solving.

evaluation of hires is the hardest problem, the largest inefficiency in the market BY FAR so much at stake and so hard to reverse, you act on little information, still the best hires are those not yet priced in any filtering tips on other than @elonmusk ask 'why' 5 times?

@one_mtb hit up @zackpaid

Spent $50k on Meta ads for my app in the last few months, but I still haven’t figured out these things 👇 - Static ads - Accurate attribution - Web to app funnels If you have good insights on any of these, shoot me a DM, will pay for your time.

@justinskycak @josh_teyler @pangram can absolutely confirm that @justinskycak has been putting out long-form bangers since way before LLMs / GPTs were good enough at writing.

I wrote the above entirely manually. Go ahead and @pangram my post above. I have a gigantic corpus of previous writing to draw from in my blog, books, and pre-2026 tweets, all manually written. Here's the full blog post that the long tweet above came from: justinmath.com/which-cognitiv… (This year I have started using AI to extract themes / takes from my corpus of previous writing for short tweets here.)

The knowledge graph is the main ingredient in our secret sauce that empowers students to learn at breakneck speed. Here's the rest of the recipe. Here's the physics of learning, and why almost no one uses it. * * * It’s shocking how much we know about how learning happens, all the way down to the mechanics of what’s going on in the brain. And not just how learning happens, but also, what can be done to improve learning. There are plenty of learning-enhancing practice strategies that have been tested scientifically, numerous times, and are completely replicable. They might as well be laws of physics. For instance: we know that actively solving problems produces more learning than passively watching a video/lecture or re-reading notes. (To be clear: active learning doesn’t mean that students never watch and listen. It just means that students are actively solving problems as soon as possible following a minimum effective dose of initial explanation, and they spend the vast majority of their time actively solving problems.) Another finding: if you don’t review information, you forget it. You can actually model this precisely, mathematically, using a forgetting curve. I’m not exaggerating when I refer to these things as laws of physics – the only real difference is that we’ve gone up several levels of scale and are dealing with noisier stochastic processes (that also have noisier underlying variables). * * * Okay, but aren’t these findings obvious? Yes, but… Yes, but in education, obvious strategies often aren't put into practice. For instance, plenty of classes that still run on a pure lecture format and don't review previously learned unless it's the day before a test. Yes, but there are plenty of other findings that replicate just as well but are not so obvious. Here are some less obvious findings. -- The spacing effect: more long-term retention occurs when you space out your practice, even if it's the same amount of total practice. -- A profound consequence of the spacing effect is that the more reviews are completed (with appropriate spacing), the longer the memory will be retained, and the longer one can wait until the next review is needed. This observation gives rise to a systematic method for reviewing previously-learned material called spaced repetition (or distributed practice). A "repetition" is a successful review at the appropriate time. -- To maximize the amount by which your memory is extended when solving review problems, it's necessary to avoid looking back at reference material unless you are totally stuck and cannot remember how to proceed. This is called the testing effect, also known as the retrieval practice effect: the best way to review material is to test yourself on it, that is, practice retrieving it from memory, unassisted. -- The testing effect can be combined with spaced repetition to produce an even more potent learning technique known as spaced retrieval practice. -- During review, it's also best to spread minimal effective doses of practice across various skills. This is known as mixed practice or interleaving -- it's the opposite of "blocked" practice, which involves extensive consecutive repetition of a single skill. Blocked practice can give a false sense of mastery and fluency because it allows students to settle into a robotic rhythm of mindlessly applying one type of solution to one type of problem. Mixed practice, on the other hand, creates a "desirable difficulty" that promotes vastly superior retention and generalization, making it a more effective review strategy. -- To free up mental processing power, it's critical to practice low-level skills enough that they can be carried out without requiring conscious effort. This is known as automaticity. Think of a basketball player who is running, dribbling, and strategizing all at the same time -- if they had to consciously manage every bounce and every stride, they'd be too overwhelmed to look around and strategize. The same is true in learning. -- The most effective type of active learning is deliberate practice, which consists of individualized training activities specially chosen to improve specific aspects of a student's performance through repetition (effortful repetition, not mindless repetition) and successive refinement. However, because deliberate practice requires intense effort focused in areas beyond one's repertoire, which tends to be more effortful and less enjoyable, people will tend to avoid it, instead opting to ineffectively practice within their level of comfort (which is never a form of deliberate practice, no matter what activities are performed). -- Instructional techniques that promote the most learning in experts, promote the least learning in beginners, and vice versa. This is known as the expertise reversal effect. An important consequence is that effective methods of practice for students typically should NOT emulate what experts do in the professional workplace (e.g., working in groups to solve open-ended problems). Beginners (i.e. students) learn most effectively through direct instruction. * * * Now, this might seem like a lot of new information -- a common reaction is “Wow, the field of education is experiencing a revolution!” But here’s the thing: Most key findings have been known for many decades. It’s just that they’re not widely known / circulated outside the niche fields of cognitive science & talent development, not even in seemingly adjacent fields like education. These findings are not taught in school, and typically not even in credentialing programs for teachers themselves – no wonder they’re unheard of! But if you just do a literature review on Google Scholar, all the research is right there – and it’s been around for many decades. Naturally, this leads us to the following question: Why aren't these key findings being leveraged in classrooms? Why do they remain relatively unknown? Here are a handful of reasons that I’m aware of. * * * 1. Leveraging them (at all) requires additional effort from both teachers and students. In some way or another, each strategy increases the intensity of effort required from students and/or instructors, and the extra effort is then converted into an outsized gain in learning. This theme is so well-documented in the literature that it even has a catchy name: a practice condition that makes the task harder, slowing down the learning process yet improving recall and transfer, is known as a desirable difficulty. Desirable difficulties make practice more representative of true assessment conditions. Consequently, it is easy for students (and their teachers) to vastly overestimate their knowledge if they do not leverage desirable difficulties during practice, a phenomenon known as the illusion of comprehension. However, the typical teacher is incentivized to maximize the immediate performance and/or happiness of their students, which biases them against introducing desirable difficulties and incentivizes them to promote illusions of comprehension. Using desirable difficulties exposes the reality that students didn’t actually learn as much as they (and their teachers) “felt” they did under less effortful conditions. This reality is inconvenient to students and teachers alike; therefore, it is common to simply believe the illusion of learning and avoid activities that might present evidence to the contrary. * * * 2. Leveraging cognitive learning strategies to their fullest extent requires an inhuman amount of effort from teachers. Let’s imagine a classroom where these strategies are being used to their fullest extent. -- Every individual student is fully engaged in productive problem-solving, with immediate feedback (including remedial support when necessary), on the specific types of problems, and in the specific types of settings (e.g., with vs without reference material, blocked vs interleaved, timed vs untimed), that will move the needle the most for their personal learning progress at that specific moment in time. -- This is happening throughout the entirety of class time, the only exceptions being those brief moments when a student is introduced to a new topic and observes a worked example before jumping into active problem-solving. Why is this an inhuman amount of work? -- First of all, it's at best extremely difficult, and at worst (and most commonly) impossible, to find a type of problem that is productive for all students in the class. Even if a teacher chooses a type of problem that is appropriate for what they perceive to be the "class average" knowledge profile, it will typically be too hard for many students and too easy for many others (an unproductive use of time for those students either way). -- Additionally, to even know the specific problem types that each student needs to work on, the teacher has to separately track each student's progress on each problem type, manage a spaced repetition schedule of when each student needs to review each topic, and continually update each schedule based on the student's performance (which can be incredibly complicated given that each time a student learns or reviews an advanced topic, they're implicitly reviewing many simpler topics, all of whose repetition schedules need to be adjusted as a result, depending on how the student performed). This is an inhuman amount of bookkeeping and computation. -- Furthermore, even on the rare occasion that a teacher manages to find a type of problem that is productive for all students in the class, different students will require different amounts of practice to master the solution technique. Some students will catch on quickly and be ready to move on to more difficult problems after solving just a couple problems of the given type, while other students will require many more attempts before they are able to solve problems of the given type successfully on their own. Additionally, some students will solve problems quickly while others will require more time. In the absence of the proper technology, it is impossible for a single human teacher to deliver an optimal learning experience to a classroom of many students with heterogeneous knowledge profiles, who all need to work on different types of problems and receive immediate feedback on each attempt. * * * 3. Most edtech systems do not actually leverage the above findings. If you pick any edtech system off the shelf and check whether it leverages each of the cognitive learning strategies I’ve described above, you’ll probably be surprised at how few it actually uses. For instance: -- Tons of systems don't scaffold their content into bite-sized pieces. -- Tons of systems allow students to move on to more material despite not demonstrating knowledge of prerequisite material. -- Tons of systems don't do spaced review. (Moreover, tons of systems don't do ANY review.) Sometimes a system will appear to leverage some finding, but if you look more closely it turns out that this is actually an illusion that is made possible by cutting corners somewhere less obvious. For instance: -- Tons of systems offer bite-sized pieces of content, BUT they accomplish this by watering down the content, cherry-picking the simplest cases of each problem type, and skipping lots of content that would reasonably be covered in a standard textbook. -- Tons of systems make students do prerequisite lessons before moving on to more advanced lessons, BUT they don't actually measure tangible mastery on prerequisite lessons. Simply watching a video and/or attempting some problems is not mastery. The student has to actually be getting problems right, and those problems have to be representative of the content covered in the lesson. -- Tons of systems claim to help students when they're struggling, BUT the way they do this is by lowering the bar for success on the learning task (e.g., by giving away hints). Really, what the system needs to do is take actions that are most likely to strengthen a student's area of weakness and empower them to clear the bar fully and independently on their next attempt. Now, I’m not saying that these issues apply to all edtech systems. I do think edtech is the way forward here – optimal teaching is an inhuman amount of work, and technology is needed. Heck, I personally developed all the quantitative software behind one system that properly handles the above challenges. All I’m saying is that you can’t just take these things at face value. Many edtech systems don’t really work from a learning standpoint, just as many psychology findings don’t hold up in replication – but at the same time, some edtech systems do work, shockingly well, just as some cognitive psychology findings do hold up and can be leveraged to massively increase student learning. * * * 4. Even if you leverage the above findings, you still have to hold students accountable for learning. Suppose you have the Platonic ideal of an edtech system that leverages all the above cognitive learning strategies to their fullest extent. Can you just put a student on it and expect them to learn? Heck no! That would only work for exceptionally motivated students. Most students are not motivated to learn the subject material. They need a responsible adult – such as a parent or a teacher – to incentivize them and hold them accountable for their behavior. I can’t tell you how many times I’ve seen the following situation play out: -- Adult puts a student on an edtech system. -- Student goofs off doing other things instead (e.g., watching YouTube). -- Adult checks in, realizes the student is not accomplishing anything, and asks the student what's going on. -- Student says that the system is too hard or otherwise doesn't work. -- Adult might take the student's word at face value. Or, if the adult notices that the student hasn't actually attempted any work and calls them out on it, the scenario repeats with the student putting forth as little effort as possible -- enough to convince the adult that they're trying, but not enough to really make progress. In these situations, here’s what needs to happen: -- The adult needs to sit down next to the student and force them to actually put forth the effort required to use the system properly. -- Once it's established that the student is able to make progress by putting forth sufficient effort, the adult needs to continue holding the student accountable for their daily progress. If the student ever stops making progress, the adult needs to sit down next to the student again and get them back on the rails. -- To keep the student on the rails without having to sit down next to them all the time, the adult needs to set up an incentive structure. Even little things go a long way, like "if you complete all your work this week then we'll go get ice cream on the weekend," or "no video games tonight until you complete your work." The incentive has to be centered around something that the student actually cares about, whether that be dessert, gaming, movies, books, etc. Even if an adult puts a student on an edtech system that is truly optimal, if the adult clocks out and stops holding the student accountable for completing their work every day, then of course the overall learning outcome is going to be worse.

@DavidSHolz regularly practice the four brahmavihārā in between to appreciate the miracle of human life and make life better for others, too.

my friends are all feeling extremely productive and also extremely drained with the latest coding models. this makes me feel like something is wrong, and also that there might be a big opportunity. does anyone have any strategies they use to make it feel better day-to-day?

@pbakaus @pbakaus love your work, congrats! just fyi the link to the open roles is not working - you've linked the .com instead of the .ai domain :)

I told you all!!! @HunterBiden gl & hf!

@JinjingLiang UI for Sites is partly better due to partnership with Webflow x.com/webflow/status…

GPT-5.6 is going to be very good at UI. My evidence: 1. The Codex app actually looks good. Much better than anything GPT-5.5 has made for us. They must be using GPT-5.6 internally. 2. OpenAI just shipped “Sites.” You don’t ship a feature for publishing AI-generated UIs unless you’re pretty confident the model can make good UIs. 3. GPT-5.5 is already strong at almost everything except UI. UI is the last obvious gap.

@acceler8future @moizali Nice little town. We personally prefer to stay in agriturismos around the Parco Regionale della Maremma, as we like to hike to Spiaggia di collelungo (one of Tuscany's few unspoiled beaches) italia.it/en/tuscany/flo…

@memeticweaver @moizali I have this area noted - Porto ercole?

Where is the best place to spend a month in Europe for the summer? Ideally: - On some body of water so you can swim easily (No paris or london) - Near or in city so you can still enjoy people and things other than swimming - Not overrun by tourists, but still has tourists so there are things to do (No Barca for this reason) - Affordable so you aren't spending bananas like $3000 a night (No Lake Como/Saint Tropez)

@signulll Ozempic and AI begin to erode OnlyFans earnings, so low-earning OF women have to find mates again.

there is a singular reason why lingerie is back & if you had figured out the connection here by mapping tech to culture, then you would’ve made a lot of money. anyone want to guess why victoria secret & the broader lingerie market is back??

@georgepickett they have limits for gpt-5.5 pro extended unfortunately - OAI confirmed that to me when I got rate limited for +2 days after 50ish messages with the model. also just fyi watch out with codex agents using chatgpt, that can get your sub + account cancelled.

Results from GPT 5.5 extended pro blows my mind every time. So underrated I’ve probably 5x’d my usage in the last month Experimenting with a system where codex controls pro thru the in app browser and follows up Higher intelligence and “free” tokens (haven’t hit limits yet)

@_aaronpaul25 warmup

Glam Up hit $1.8m ARR in 8 months. Sprout hit $3m ARR in 7 months. The key to that was our UGC playbook. I'm finally dropping the playbook and lowkey I'm scared to drop this. You'll understand why once you read it. It's gonna be three parts but here's part 1. Part 1 itself is 40 pages long. I made sure it's tactical advice and upfront with no BS. Bonus: repost + reply 'warmup' and I'll DM you the Account Setup + Warmup module from our internal creator course. Must be following so I can DM. prep-ai.typeform.com/to/X7g0nh6E

imo he does it for the love of the game. he’s succeeding because he is: - genuinely curious and well-read which allows him to make interesting arguments and observations across fields - observing and absorbing how other smart people approach content - truly plugged into X meme culture and can make self-deprecating jokes - able to think outside of VC cargo cult thought - surrounded by people who can share genuinely helpful feedback

why is marc andreessen on a generational PR run? is it because he's trying to trade his worthless cash for your valuable businesses? someone explain like i'm 5

@seeingleaves @patrickc @RuxandraTeslo I don’t think so

@memeticweaver @patrickc @RuxandraTeslo I believe he was joking.

València: street with old buildings vs street with new ones. Why is everything built in the modern era so distasteful?