bartwr

I am learning about radio and modulation using SDR (with professor Claude - incredible for learning! Especially if you know exactly what you want to learn and have a strong enough background in somewhat related fields - audio and image DSP for me - to make exercises it writes for me short, relatable, and yet illuminating) and our second lesson after cheap 433 remotes was FLEX and pagers - plenty of that stuff in the city, with about half messages from a hospital a few blocks away. Still very widely used in the US (I live in NYC). Seeing what gets broadcasted there 100% unencrypted was quite a shock. Patient names, ages, and conditions; doctor names, rooms, departments, requests for medications and assistance... Insane amount of super private PII.

I'm generally skeptical and critical of most things that Meta is doing ("fool me once...") and I definitely won't trust them enough to use their agents and give them any more data than I already do, but this is a super cute and fun idea!

I love microcontrollers and DIY IoT as a hobby, a huge fan of ESP32s (so affordable and perfectly packaged in ready-to-use boards), and I can see how such easy, vibe-coded projects can inspire more people and show them how easy it is to get started with them today. (I remember my first experiments with microcontrollers and DSPs at college in 00s - the nightmare of even just flashing those devices, not to mention the almost-prohibitive cost to get them and the sometimes required JTAG programmers I faced in Poland... My BSc thesis project was reduced in scope a few times because my professor couldn't get some purchases and shipments approved on time)

A toy with not much value? Sure. But who cares, it's fun and inspiring! They even recommend vibe-building so that the user doesn't need to download or configure SDKs. Awesome stuff for complete beginners to this area.

Over a decade ago, I could see Google releasing such a whimsy, hackersy fun toy project. Sadly, not anymore... (Saying this as a former Google employee in 2017-2022, witnessing in person the sad, gradual change from hacker culture to corporate culture)

I have most likely outdated info, I left Google Research 4y ago. Back then, available TPU instances were plenty and GPU scarce. Nobody wanted to mess with an immature crashing compiler and very steep performance cliffs (performance was excellent only if you stayed within the guardrails, and being outside was supported and not even resulting in a warning - as it was so common in code). But I believe most of it has changed for the better for TPUs.

My experience cycling regularly in NYC: bike lanes separated by curb, stoppers, or poles are more dangerous as cars stop at their entrances/exits and I am literally trapped or cannot enter them before/after an intersection. I'm not against them in principle, but without extremely strict enforcent of laws (let's say a ticket 5% of someone's annual income and a loss of DL on a repeated offense - this stuff endangers people's lives), they are sadly counterproductive. :(

I love using AI tools and they are changing my work and life in amazing ways. I cannot imagine going back. And yet, I am more concerned about the social damage due to their widespread use and the amounts of slop they generate. Just this week: - There was an article about a news company faking polls by asking LLMs for answers. - My wife told me that she stopped watching any funny pet videos because 99% now is AI slop - start normal, but then turn into someone's slop idea. - A friend told me their big tech company uses AI-generated metrics as part of performance evaluation. Nobody checks them. - Another friend told me their big tech company requires engineers to use AI-generated commit messages with terrible signal-to-noise ratio and making version control and history useless for engineers. But directors and PMs love them, they are so descriptive! - My neighbor uses LLMs to create some neighbor meeting plans/agendas, plausibly looking PDFs citing contractors etc. It's impossible to read through it, mixed hallucinations and real information, all wrapped in thousands of slop words. What is real and what made up? I'll spend 10x more time double guessing. - Encountering more and more articles and general "content" that is AI generated and looks ok at the first glance, but slop upon inspections. Why would I read LLMs output on a webpage with ads, if I can ask it myself and get better, personal answers and style?

And I am not even talking here about other ethical issues, training data, less junior job positions, job replacement of journalists with LLM-equipeed contractors, etc.

LLMs make my personal and work life so much better, but social life unbearable. Is it worth the trade-off? I guess it doesn't matter at this point.

It's hilarious that people describe their anecdotal experience of being in a calorie deficit as a proof that "intermittent fasting works". This was never the question, but whether intermittent fasting brings additional weight loss benefits as compared to calorie deficit with frequent meals.

My anecdotal experience from 20y of bodybuilding and doing ~3 cuts a year: for cutting, I tried IF, 6 meals a day, low fat, low carb, high fat true keto, balanced... everything works. And works equally well - this is backed by numerous studies. The only difference is the impact on health parameters (different will get worse on low fat vs high fat), satiety, and how easy it is for someone to sustain the diet and stay in a deficit. This will depend on the lifestyle and personal preferences. So my preferred way to cut is high protein, low carb, essential fats, a ton of fiber. When building muscle I go high everything but balanced.

Anything else and more is sectarianism and people bragging about their choices not having verified their true claimed efficacy or benefits.

I share the sentiment. I haven’t used it in a while (at work use different languages and in the last few years my personal coding is only Python script/Jupyter notebook bite-sized), but anytime I hop into it, it immediately "clicks" and gives a comfortable feeling, despite changing over years. A perfect language for medium sized relatively clean and mature personal or small team projects.

Frictionless, pleasant, not thinking too much how to express things (and still managing to write them reasonably idiomatic), tends to support clean encapsulated code, quite rich environment/libraries, great tools (debuggers, profilers), safe, relatively fast, not many foot guns, zero build setup, on small project zero build times, trivial to create good functional simple UI, can get fancy dynamic with reflection when I need "magic".

Basically not many pain points that would make me rage quit and almost everything I'd want is simple to achieve.

Original author here and it's been a while since I have read such word salad nonsense, sorry. Why people who have no idea or expertise comment on articles? GenerateMips API constructs a mip chain by using box/bilinear (equivalent for factors of two) log N times.

Trilinear interpolates across three dimensions, such as 3D textures or mip chains. It is not a method for downsampling, but a method for filtering that interpolates two bilinear results, such as two bilinear filters of mip levels that were generated with "some" downsampling filter (which can be anything from box to Lanczos).

Anisotropic is a hybrid between trilinear across a smaller interpolation axis under perspective projection of a 3D asset and multiple taps along the longer axis. (More expensive)

It wasn't an exaggeration! :) The shock of many of my colleagues (often not even junior... sometimes professors who decided to join the industry) "wait, I need to talk to product teams and ask them about their needs, requirements, trade-offs, and performance budgets and cannot just show them my 'amazing' new toy experiment I wrote a paper about that costs 1000x their whole budget and works 50% of time, and they won't jump to putting it into production?" was real. :) They don't want to think about products and talk to product teams (but get evaluated based on research that gets into products and makes a difference there), just do Ivory tower own research.

One of many reasons why Google invented Transformers and many components of GPT pre-trainint, but ChatGPT caught them "by surprise" many years later.

You don't understand the Silicon Valley grind mindset :) I personally agree with you - I am happy working on interesting stuff, getting a good salary, and don't need a promo. Most times I switched jobs it was a temporary lowering of my total comp and often the level. But most Googlers are obsessed with levels/promotion, talk about it, and the frustration is real. They are hyper ambitious and see level as their validation.

And if you join as a PhD fresh grad (RS or SWE), L4 salary is ok, but not amazing compared to costs of living there. From L6 on it starts to be really really good.

If someone believed they will earn 2-5x better than in academia, with full freedom to work on whatever interests them, and no need to deliver value to the employer... Well, let's say "ok", we have all been young and naive, but if their advisors have not adjusted their expectations, they are at fault, maybe even fraudulent.

Even being in elite research groups at the most prestigious companies you are evaluated on product and company Impact, which has nothing to do with how groundbreaking your research is, how many awards it gets, or how many cite it. I had colleagues at Google Research bitter that I was getting promoted (doing research addressing product needs - and later publishing it, "systems" papers that are frowned upon by "true" researchers), while with their highly cited theoretical papers they would get a "meet expectations" type of perf eval and never a promotion.

One unique and new feature of Slang that sets it apart from existing shading languages is support for differentiation and gradient computation/propagation - while still cross-compiling generated forward and backward passes to other, platform-specific shading languages. Before, the only way to backpropagate through shader code (such as material BRDF or lighting computation) was to either manually differentiate every function and chain them together, or rewrite it in another language or framework - such as PyTorch or a specialized language/framework like as Dr.Jit, and keeping both versions in sync after any changes. Game developers typically don't use those, programming models are different (SIMT kernels vs array computations), it's a maintenance headache, and it was a significant blocker for a wider adoption of data-driven techniques and ML in existing renderers or game engines.

It's interestingly disingenuous that many claim of GLP-1 agonist miraculous effects on all kinds of health problems, where the same problems are "simply" solved by getting on a calorie deficit and lean. Liver, kidneys, heart, etc. If you have a non-alcoholic fatty liver disease and are obese, getting leaner will heal it. All those impressive results are on obese or diabetic people. So it is not only not a surprise, but also dishonest marketing or ignorance.

Don't get me wrong - those are miraculous drugs. First real non-stimulant low side effect appetite suppresion that will help millions. But let's wait for honest research on lean people before spreading marketing on how it improves overall health.

Also, how nobody mentions the need for increasing the dosage and tolerance build-up (just check reddits how much people end up having to take after months of continuous use). You cannot be on it "for life".

Yes, I am an (amateur) photographer for the last 27 years, from film, DSLRs, mirror less, mobile. And I worked on camera ISPs - both hardware modules, saving RAW files on mobile for Google Pixel, as well as software processing of RAWs.

But I guess you know better than me. ¯\_(ツ)_/¯