What public figures publish and believe, in their own words.
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The winning system of the next decade will not be the one with the most aesthetically pleasing buttons, nor will it be the one with the fewest screens. It will be the system that best understands the human’s “job to be done,” autonomously selects the right tools on their behalf, clearly shows the user what is about to happen, and gracefully recovers when the user’s context is incomplete or ambiguous.
Another conclusion might be that the online ecosystem has become so polluted-so fragmented, deceptive, overstimulating, ersatz-that it has warped our ability to exercise taste at all.
A.I. companies need to associate themselves with taste precisely because their tools are not very palatable, much less cool, to anyone outside of Silicon Valley.
This is not the case with many other technology driven industries where capability or advancements often tend to be longer held and a fast-follow model is harder.
While not technically a piece of gear, travel insurance is something you need on the road. In fact, it’s the MOST IMPORTANT thing you need, because it protects all your other gear.
Away is my go-to brand for durable, quality luggage. Their hard shell carry-on suitcases are lightweight and fit perfectly into overheard bins, ensuring you have plenty of room for all your gear while still letting you travel carry-on only.
If you want something better than your phone but still easy to use, get a GoPro. They're durable and take incredible photos and videos without a steep learning curve.
The wireless Bose QuietComfort 45 headphones are fan favorites and my go-to brand. They are comfortable, rechargeable, and do an amazing job at removing background noise.
One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity.
Worse, you realize that the gazillions of unit, snapshot, and e2e tests you had your clankers write are equally untrustworthy. The only thing that's still a reliable measure of "does this work" is manually testing the product.
You can give it a Bash tool so it can ripgrep its way through the codebase. You can give it some queryable codebase index, an LSP server, a vector database. In the end it doesn't matter much. The bigger the codebase, the lower the recall. Low recall means that your agent will, in fact, not find all the code it needs to do a good job.
And organizations have super high pain tolerance. But human-made enterprise codebases take years to get there. The organization slowly evolves along with the complexity in a demented kind of synergy and learns how to deal with it. With agents and a team of 2 humans, you can get to that complexity within weeks.
With an orchestrated army of agents, there is no bottleneck, no human pain. These tiny little harmless booboos suddenly compound at a rate that's unsustainable. You have removed yourself from the loop, so you don't even know that all the innocent booboos have formed a monster of a codebase. You only feel the pain when it's too late.
An agent has no such learning ability. At least not out of the box. It will continue making the same errors over and over again. Depending on the training data it might also come up with glorious new interpolations of different errors.
You can staff a school with the best teachers on earth, give them unlimited resources, and wrap the place in every evidence-based intervention imaginable, and it still won’t work if students are resistant, disengaged, or actively hostile to the enterprise.
Education is not something that can be done to someone; it’s something that requires at least a minimal act of will from the learner, and no reform agenda can engineer that away.
And so this loop, this cycle, is gonna go on and on and on. It kinda comes down to basically intelligence is gonna scale by one thing, and that's compute.
And so the next scaling law is the agentic scaling law. It's kind of like multiplying AI. Multiplying AI, we could spin off agents as fast as you want to spin off agents. And so, you know, I… You know, I have four scaling laws.
The amount of data that we use to train models is going to continue to scale to the point where we're no longer limited… Training is no longer limited by… Data is now limited by compute. And the reason for that is most of the data is synthetic.
They come to a computing platform because the install base is large. Because a developer, like anybody else, wants to develop software that reaches a lot of people. So, the install base is, in fact, the single most important part of an architecture.
so many times that process has been managed by humans because you're selecting the right candidate build and you're verifying it and you're thinking, you know, you're figuring out cherry picks and then you like rebundle and then you send it out and that doesn't scale if you have one or two or a handful of people trying to make group decisions and do group sense making.
And not only that, but like humans were already a bit of a bottleneck in the review process. Now it can be worse because things that we fairly straightforward changes that some companies had automation around reviewing, they've removed that reviewing because if AI is involved and they're worried about the verifiability or the reliability of the code.
There were a handful of people probably managing a security review process or a launch process or a deployment process or, you know, sometimes reviews were a little slow and they got backed up. Well, now we just threw gas on the fire and so all of that is a problem.
And so, like in the immediate term, yeah, we were getting more out, but now our systems, whether technology systems or human systems or processes, are really kind of getting overwhelmed.
these apps that are on the app store for using these smart home devices, etc. Uh, these shouldn't even exist kind of in a certain sense. Like shouldn't it just be APIs and shouldn't agents be just using it directly?
I think to a large extent you feel like it's a skill issue. It's not that the capability is not there. It's that you just haven't found a way to string it together of what's available.
I kind of went from 80/20 of like, you know, uh to like 20/80 of writing code by myself versus just delegating to agents. And I don't even think it's 20/80 by now. I think it's a lot more than that. I don't think I've typed like a line of code probably since December basically.
Um we are seeing a lot fewer sort of pure AI solutions now where um they are just one shots the problem. Um so so there was there was a month where that happened and and that has stopped.
you know, right now we're going through uh an in um an cognitive version of the Copernican revolution where we used to think that human intelligence is the center of the universe. And now we're actually seeing that there's there's very different types of intelligence um that that that are out there uh with very different strengths and weaknesses.
So, you you can't look at any given scientific achievement purely in isolation and give it an objective grade without being aware of the context both in the the past and the future. And so it it it may never be something that you can just reinforcement learn the same way that that you can for much sort of more localized problems.
So, we're now in a situation where suddenly people can generate thousands of theories for a given scientific problem. And now we have to to verify them, evaluate them and this is something which we we have to to change our structures of science to actually sort this out.
So, I think AI has basically driven the cost of idea generation down to almost zero in a very similar way to how the internet drove the cost of communication down to almost zero, which is an amazing thing, but it you know, it it it doesn't make it doesn't create abundance by itself.
The competitive moat for a business right now is not the use of AI. It's human-originating, high-quality, high-fidelity data that other systems can't replicate - even if they implement the exact same features and are built by the same agentic systems.
And what you can do if you've got one of these conformance suites is you can give it to a a good agent and say, "Write code until this test suite passes." And it kind of will.
just cuz the test suite passes doesn't mean that the web server will boot. You know, there's there's always a chance that when you actually try in the real world, something's not going to work.
LLMs make it easy for anyone to add new features to the product. Even before AI, teams were battling feature creep, where the product becomes increasingly complex, bloated, and cluttered.
players who are also commentators give better commentary than people who are just commentators. Stock analysts who never invest have zero skin in the game.
Because Enthropic saw it before Google. And then Google had Nano Banano and Gemini 3 which caused their user metrics to skyrocket and leadership at Google was like oh and then they started making the statement of we have to double compute every is it 6 months or I don't remember the exact number that they said.
TSMC is much more excited to give allocation to Graviton than they are to tranium because they view CPU business as more stable long-term growth right and as a company that is conservative and doesn't want to ride cycles of growth too hard you actually want to allocate to the uh the market that is more stable and lower growth rate first before you allocate all the incremental capacity to the fast growth rate market.
Um I think at least this year we're going to see margins for the model vendors go up a lot, right? Because they're so capacity constrained, they have to demand destroy demand, right? there is there's no way they can continue anthropic can continue at the current pace without destroying demand.
A companies that have locked up, you know, and and don't have commitment issues, you know, have these 5-year contracts for compute, they've kind of locked in a humongous margin advantage because they've locked in compute for 5 years at a price of what it transacted at 5 years ago or three years ago or two years ago, whatever it is.
if improvement stopped you know here the value of an H100 is now predicated on the value that GPD 5.4 four can get out of it instead of the value that GP4 can get out of it and the margins and all that stuff that these labs are doing and they're in a competitive environment so their margins can't go to infinity. Um so you sort of have this like dynamic that is quite interesting in that an H100 is worth more today than it was 3 years ago.
If you run a software business that is purely transformative - that takes incoming data, does something to it, and turns the data back out - that will be a problem.
I believe that data - real-world data, mostly human-generated, validated, and cleaned - is the only reliable moat we have as software founders in the near and mid-term future.
Everybody thinks our schools are in crisis all the time because they’re being forced to do something they were never meant to do, which is to make everyone college-ready, and they’re being forced to do that because we have seen jobs that provide a living wage without a college diploma evaporate.
But please, stop saying things like “I just want us to get back to a world where kids were graduating high school with basic skills!” Because the world you’re referring to never existed.
And it went from this kind of thing that I didn’t believe into to actually, like, a core part of who I am today as a leader, as a game designer, as a game director. And some of the best ideas have come from developing other peoples’ ideas
That is the best prop artist in the industry. That’s who’s gonna show up on our doorstep, so when they show up here, we should treat them like the best prop artist in the industry instead of starting from a place of doubt and cynicism.
And there’s a weird thing that happens that’s just kind of a human nature thing. The less you interact with somebody, the more you sort of become alienated from them and vilify their point of view.
I'm mad at Amazon for laying off 16,000 people and blaming AI without an AI strategy for it. Those people are not going to be able to find jobs, by and large, and they're the first of many to come and nobody has a plan for this.
And so, what's happening is everybody on average is setting that dial to about 50% and we're going to lose about half the engineers from big companies, which is scary.
Because they're all starting to spend their own salaries in tokens. And so, at least for a while, if you want your engineers to be as productive as possible, you're going to have to get rid of half of them to make the other half maximally productive.
I had a lot of my own ego and identity wrapped up in my sort of compiler background. It's all It's interesting, right? But it's it's not useful in any meaningful sense anymore.
they they're like, "Yeah, you know, I use Cursor, and I I ask it questions sometimes, and I'm really impressed with the answers, and then I review its code really carefully, and then I check it in." And I'm like, "Dude, you're going to get fired, and you're one of the best engineers I know."
But, your 'calling' will never involve constant discomfort. If you are passionate about a political issue but you hate crowds, you do not have to go to protests.
There were booms and busts, and cumulative price inflation was zero, and the United States went from a new-world emerging backwater in the late 1700s to being the world's largest economy by the late 1800s.
Another way of putting it is that a collection of dozens of solvent well-run banks can still be broken as a group if they become greatly overleveraged compared to how many depositors want their money back at once.
That's due to the fractional reserve structure of banks. They're unstable and reliant on each other by design, more closely than companies in most other industries.
This, in turn, creates serious estimation problems for the diversion rate, which is why environmental benefit-cost analyses underrate the effect of urban rail construction.
Finally, I’d like to thank the authors of Roastguide for the amazing work they’ve done on their app, the attention they pay to their community on Discord
Polyvagal theory provided me with a coherent framework for understanding why cognitive insight alone often fails when a nervous system is in defensive dominance. In such states, higher-order cortical processes are functionally constrained.
The central clinical proposition of polyvagal theory-that autonomic state functions as an organizing platform shaping perception, emotion, and relational behavior-was not empirically disproven in the critique.
Why would we lock ourselves into someone else's proprietary binary? Control over our data, and control over how we view and engage with it, feels like it will be utterly non-negotiable going forward.
I've always been opinionated about how software should work. Mainly, it should be fast. The bounds of it should be "knowable." The contract you have with it should be "sane" (i.e., you just own it).
With Claude, I've built a host of software like this. Mostly small tools for myself — programs that instantly append copy buffers to text files (I keep a running file of nice things people write to me called notapieceofshit.txt) or quickly perform live currency conversions.
Many AI researchers are overly focused on risks from model misalignment, and will be in for a rough surprise when havoc arises from other layers of the stack.
Lastly, the duration of a clinical trial does not merely determine how fast an individual therapy reaches patients. It also shapes which diseases attract serious investment and which do not.
Trials serve two distinct functions: validation — confirming whether a drug works and is safe — and learning, or generating biological data to refine our understanding of a disease, a compound, and the relationship between the two.
Being a great listener when people speak. Deep insights about understanding, connection, helping people express themselves, overcoming assumptions, the ethics of gossip, and more. Specific techniques for the support response, encouraging elaboration, and keeping it balanced. You can’t be ethical without being a good listener. When people say, “I can’t talk right now,” what they really mean is “…
At Anthropic, we don't build for the model of today, we build for the model of six months from now. And that's still my advice to founders that are building on LLMs.
History has shown very extensively that, left to their own devices, even if something could be done safely, people in a hurry will find all sorts of ways to f*** things up.
I think frontier AI auditing (covering both safety and security) is both urgently needed and doable, and can help avoid extreme concentration of power, since it introduces external oversight into the decision-making of people with a "country of geniuses in a datacenter" at their disposal.
We need to accept that at best, we will just barely avoid some of the worst case scenarios (e.g., an AI-enabled biological weapon that kills billions, a rogue AI takeover, or stable global totalitarianism enabled by AI), given the current pace of AI capabilities relative to the pace of governance.
I think everyone’s starting point for investing should be to know and be in the portfolio that is best to have, independent of any tactical views of the markets.
If you want my advice about how to do these things well to help you be successful at investing, I recommend the Dalio Market Principles course which is put out by the Wealth Management Institute of Singapore.
Extrapolators have a remarkable track record in the AI field, being repeatedly early to trends and capabilities that empiricists believed were still decades away.
I initially assumed this was a temporary divide. New users tend to watch closely and check the system's progress, but as trust builds, that scrutiny fades and monitoring starts to feel like a chore. Yet it still seems like there's two camps (for now).
Like any addictive consumer product that routinely exposes users to graphic sex, extreme violence, and anonymous sexual predators, social media in its current form causes a variety of harms to people of all ages. But we allow adults (often defined as age 18) to make decisions that are bad for them.
I cannot support any law that includes a “parental consent” exception—it defeats the central purpose of the law by dropping parents and children straight back into the same collective-action trap
Sixteen is also roughly the age by which a majority of adolescents have completed puberty: a sensitive period of neural reorganization during which it is extremely important to protect the brain.
And that risks giving the impression that basic constitutional rights and restraints on police and executive power that date back to the Founding are, actually, negotiable.
Yes, I could totally see how OpenClaw could become a huge company. And no, it’s not really exciting for me. I’m a builder at heart. I did the whole creating-a-company game already, poured 13 years of my life into it and learned a lot. What I want is to change the world, not build a large company and teaming up with OpenAI is the fastest way to bring this to everyone.
My one little bit, the one little bit of of fundamental uncertainty even on long time scales is this thing about tasks that aren't verifiable. Like, planning a mission to Mars, like, uh you know, doing some fundamental scientific discovery like like CRISPR, like, you know, writing a writing a novel. Hard to hard to verify those tasks.
on the basic hypothesis of you know, as you put it, within 10 years we'll get to, you know, you know, what I call kind of country of geniuses in a data center. I'm at like 90% on that. Um and it's hard to go much higher than 90% cuz the world is so unpredictable.
the goal is not to teach the model every possible skill within RL just as we don't do that within pre-training, right? Within pre-training, we're not trying to expose the model to, you know, every every possible you know, way that words could be put together, right? You know, we're it's it's rather that the model trains on a lot of things and then and then it reaches generalization across pre-training, right?
I think there's something going on that pre-training it's it's not like the process of humans learning. It's somewhere between the process of humans learning and the process of human evolution.
I will tell you though what the most surprising thing has been. The most surprising thing has been the lack of public recognition of how close we are to the end of the exponential.
So, I feel we, as a society, we need some catching up to do in terms of understanding that AI is incredibly powerful, but it's not always right. It's not, it's not all-powerful, you know?
But I don't want to, like, pull that down because every time someone made the first pull request is a win for our society, you know? Like, it… Like, it doesn't matter how, how shitty it is, y- you gotta start somewhere.
If you get really good at coding that means you have to be really good at general purpose problem solving. So that's a skill, right? And that just maps into other domains.
Long lead times and soft costs, fueled by the world-leading US wages for "white collar" work, are the root cause of poor performance in low-volume production because there are few units to spread the soft costs over.
there’s like a line to walk between being seriously concerned, but not fearmongering because fearmongering destroys the possibility of creating something special with a thing.
It’s actually how good they are at programming is almost a burden in their ability to empathize with the system that’s starting from scratch. It’s a totally new paradigm of, like, how to program. You really, really have to empathize.
my criticism of MoltBook is that I believe a lot of the stuff that was screenshotted is human prompted. Which, just look at the incentive of how the whole thing was used. It’s obvious to me at least that a lot of it was humans prompting the thing so they can then screenshot it and post it on X in order to go viral.
We show that equilibrium generically occurs at neither the Harberger nor Glaeser-Luttmer benchmark. Cost-minimizing suppliers drive allocations to vertices, not interiors. Corners are not an assumption but an outcome about what cost-minimizing suppliers choose. The correct benchmark is corners, not random, and corners generate qualitatively different welfare properties: losses far larger than either efficient or random distributions, and discontinuous jumps from small parameter perturbations.
These corner allocations create a distinct source of cross-market misallocation, separate from the aggregate quantity loss (the Harberger triangle) and from within-market misallocation emphasized in prior work.
Over thirteen lessons, you’ll learn about the past, present, and future of meat, from the ways animals and humans are treated in meat processing plants to emerging meat alternatives.
filmmakers Robert Kenner and Melissa Robledo reunite with investigative authors Michael Pollan and Eric Schlosser to take a fresh look at our efficient yet vulnerable food system.
Furthermore, I believe it’s very unlikely that space-based manufacturing or mining could be lucrative enough to fund the venture.
Their words now
Today, exploding demand for power-intensive AI applications provides enough of an upside to justify producing components in space, providing the economic engine necessary to justify and fund the trillions of dollars necessary to build and sustain space factories.
The economics of orbital “datacenters” or essentially glorified Starlink satellites with a bunch of GPUs attached are likely to be even better than Starlink.
building a third-party client to read and route messages is a gray area in their terms of service, and perhaps only partially supported in their API surfaces.
The "homework problem" described above might be much worse in practice for end users who aren't motivated to invest in their email triage systems, even if it will save them time in the medium term.
If you’re interested in the environmental impacts of our current food system and want to dig into the opportunities for meat-like products without the animals (and their impacts), I really recommend it.
Tariffs, and especially the erratic implementation of them, are teaching our allies and enemies alike that the US is no longer a reliable trading partner.
I think that there will be lots of work for us cleaning up after the slop, but if you know what you're doing AI augmented development is going to get you some amazing results
I've realized that performance engineering as we know it may not be enough - I'm thinking of new engineering methods so that we can find bigger optimizations than we have before, and find them faster.
The staggering and fast-growing cost of AI datacenters is a call for performance engineering like no other in history; it's not just about saving costs - it's about saving the planet.
Immediately cease trying to perform meaningful work via a chatbot (e.g. ChatGPT, Gemini on the web, etc.). Chatbots have real value and are a daily part of my AI workflow, but their utility in coding is highly limited because you're mostly hoping they come up with the right results based on their prior training, and correcting them involves a human (you) to tell them they're wrong repeatedly.
I particularly like to combine this with slower, more thoughtful models like Amp's deep mode (which is basically just GPT-5.2-Codex) which can take upwards of 30+ minutes to make small changes. The flip side of that is that it does tend to produce very good results.
As high-level programming cedes way to the prose compiler, making your goals and specs well understood to the ambiguity loop and showing good judgment is going to matter more than ever.
The Moonlight client is available for virtually every platform: Mac, iOS, Android, and of course Linux. That means no need to dual boot to enjoy the best games at the highest fidelity.
This is what it means to read a short story collection from an absolute master at the absolute peak of his powers. He can slide you frictionlessly between Icelandic troll tragedies to lethal drone-leopard romantic agonies to battles of the gods and the cigar box that has the universe inside of it. All with the lyricism of Bradbury, the madcap wit of Sturgeon, the unrelenting weirdness of Dick, the heart of Tiptree and the precision of Chiang.
Which brings me to the film adaptation of Inherent Vice, that widely-disliked, subtly-championed shaggy stoner LA noir that confused critics and rankled fans and also happens to be the best movie Paul Thomas Anderson has ever made.
If I was put on the spot to name the greatest movie of the 21st century, I would probably say Michael Hanneke's Amour, an achingly moving, relentlessly unpleasant portrait of an elderly couple sinking into illness and senescence, forced into making the grimmest decisions possible.
The Coens are typically considered to be at their best when they're cruel diagnosticians of American life - see their portentous, overstuffed, painfully self-impressed No Country for Old Men
but ice sort of forms through a process of random nucleation and then extension. And this is cool because you can uh modulate the like sort of nucleation the rate of nucleation and extension to then modulate the probability of ice formation.
but I think that the thing that a lot of people will face is just like this question of like is it worth you know traveling so far to give up my current social context and that will put a limit on like how much people want to use this for
But right now like there's no way to press pause on their biological time. So like what if you had an ambulance of the future, right? Like what if you could take someone who is on their deathbed and um you know find some way to you know just sort of hibernate them basically until um the sort of critical cure for their disease comes online.
I think longevity and aging kind of occupy this weird realm where because they're not like explicitly diseases in a way that's fully socially recognized yet they're not seen as valid to work on but like that's not really for I think technical reasons on some level it's more for like classification reasons
What you want to do is say what made you cancel? In other words, what about the product or situation or whatever caused the cancellation? Just phrasing it that way, you get much better results.
So let's suppose you add 100 customers a month and you have 5% cancellation. So 100 divided by 5% is 2,000. So a company like that will never have more than 2,000 customers.
Marketing grows only as fast as you can improve marketing. We all know that's quite hard actually. It's linear. It's hard to find new channels that aren't trivial. Like, it's hard. Of course, we're going to do it, but like it it's hard. Whereas, cancellations grow automatically as you grow, right? So, cancellations always overtake marketing for this reason.
They actually had the budget and bought the stupid thing. Then they went through onboarding and invested their time etc etc. That is a crazy gauntlet that almost no one gets through.
there's a there's a series of of questions that I ask to to diagnose why is growth slowing in this order because it's one of these things where the first one that's a problem, if you don't fix that, it doesn't matter if you fix one of the ones below.
I only checked a handful of youtube reviewers, but my favorite is Jennifer Wang, who seemed properly autistic about clothing quality and understanding that there are multiple places on the pareto frontier one might choose to occupy
The ORS is a brief, 4-item measure designed to assess areas of life functioning known to change as a result of therapeutic intervention. It takes less than one minute to complete and score.
The SRS is a brief, 4-item measure designed to assess the therapeutic alliance. It provides immediate feedback on the quality of the relationship and allows for course correction in real time.
But the goal we need to achieve is so much easier: we just need to build a model that’s as good as us at alignment research, and that we trust more than ourselves to do this research well because it’s sufficiently aligned.
Back when the Fourth Amendment was enacted, the most fundamental remedy for an unlawful government entry into your home was a civil suit for trespass against the officers.
Even if the policy is unconstitutional, as it seems to be, a person who is illegally searched probably can't sue ICE for violating their constitutional rights.
Given the focus in Coolidge, Shadwick, and Payton on the fundamental role of warrants in inserting a judicial check on the executive, it seems out of place to say that this can be satisfied by the executive checking itself.
After spending years working on health, lowering your resting heart rate (RHR) before bed is the single most effective thing you can do for your health.
If you have Bruxism, I strongly encourage you to address it. A dentist on Blueprint recommended the SomnoDent Bruxism Device , and for the first time in my life, my Bruxism has stopped.
Use red and amber lights in the evening . Bluelight is bad for sleep. You can get blue light blocking apps (f.lux), glasses and also turn red mode on your phone.
Canonical’s LXD works out-of-the-box on Ubuntu, and is a great way to sandbox an agent into a disposable environment where the blast radius is limited should the agent make a mistake.
But with today's AI coding agents, building software is remarkably easy. So instead of handing over static assets and static guidelines, designers can deliver custom software. Tools that let clients create their own on-brand assets whenever they need them.
Given the simplicity and speed of the algorithms in this post and the increasingly small deltas between successive algorithms, perhaps we are nearing an optimal solution.
My daily drivers now include Amp, Cursor, and Claude Code. I still enjoy notebooks, but only for data analysis, machine learning, or other exploratory workflows where the iterative, visual nature of notebooks shines.
Scrutiny applied to AI is way below other technologies that - even if you totally ignore the doomers - could put far fewer lives at risk than AI in a single incident.
In short, whether AI is a normal technology or an unusually dangerous one, it doesn't make sense for companies to check their own homework on safety and security.
Компьютеры стали бытовым прибором, начать программировать легче, чем когда-либо, сложнейшие вещи делаются просто и даже тривиально, написаны горы готового кода. К сожалению, у этого есть и обратная сторона - программы становятся большими, медленными, неуправляемыми, непонимаемыми.
Local-first is not going to win, but that's okay We'll explore the complexities of traditional stack (db-server-frontend), develop a theory of software evolution: which systems succeed and why.
When applications can generate capabilities on demand, the definition of "what this product does" becomes more fluid. Features aren't just what shipped in the last release, they're also what users will ask for in the next session.
Big flat toggle buttons under satisfying tension. Clear affordances. You push them down, not in. One click catches, one doesn't. You can use them with bare hands, oven mitts, fingers coated in slippery whatever. Doesn't matter. Plenty of surface area. Outstanding design. It's all been downhill from there.
I also read critic Michael Dirda's On Conan Doyle, a lovely short book I'd never heard of that I spotted on the shelf at the library — hooray for the serendipity of the stacks!
This book has more of a DGAF tone than Hold Still, I think, and it works best for me when Mann is telling stories instead of trying to extract a lesson for the reader.
The Academy Is…, one of my favorite bands from this century (and yes, I feel old just typing that out), has recorded their first new album in eighteen years, titled Almost There, and will be putting it out in March.
The main reasons for males suffering from a higher burden of global TB cases, compared to females, may be in large part due to population-scale factors, such as employment type, the quantity and type of social contacts they make, and their health-seeking behaviours (e.g. differences in diagnostic and treatment delays between genders).
When someone is asked to reflect on their own life, they draw on a wide range of experiences. When people are asked about “the world”, they recall a very small number of recent events (the availability bias).
I don’t think this is very productive (expert users of a piece of software are notoriously bad at being able to tell if an explanation will be clear to non-experts), so I needed to find a way to identify problems with the man pages that was a little more evidence-based.
if it walks like a checking account, quacks like a checking account, and is marketed as an alternative to checking accounts, then it is almost certainly within Reg E scope
A product manager will be called upon to make scores of decisions every day. In none of these cases will you have all of the information you want, and you’ll rarely be 100% confident. Poker champion turned decision researcher Annie Duke will help you learn how to be comfortable with uncertainty.
When does our animal brain make decisions for us before our more analytical brain has a chance to think through the consequences? From Nobel laureate Kahneman, this is one of the most important psychology books ever written.
The third book in Cagan’s trilogy, this one focuses on how companies can transform their cultures from project- to product-oriented. This book is probably less useful for the individual contributor, but it offers hope for anyone trapped in a feature team that something better is possible. Buy it for your boss or CEO.
Laszlo and his team at Google reinvented the role of human resources. This book is a terrific overview of what makes Google Google, from culture, to hiring, to making decisions.
Jazz is messy, and musicians seem to court disaster night after night. What can product leaders learn from how these artists approach their art? Barrett’s entertaining book formed the backbone for
The internet is unique in the history of technology because there’s a list of things it improved (communication, access to information) but another list of things it likely made worse for almost everybody
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com.
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.