Meta AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

Meta AI AI

How Much of a Shortcut Are Connections in Top AI Lab Hiring for PhD grads? [D]

hi everyone. I'm trying to calibrate my expectations and would appreciate full honest perspectives from people involved/ with experience in hiring at places like Anthropic, OpenAI, Google DeepMind, Meta, etc (haven't started interviewing yet). I'm at a top ML university, but my advisor is not particularly well known in industry and doesn't have many industry connections. Looking around, I'm seeing peers with research records that seem comparable to mine (and in some cases arguably weaker) land interviews and jobs at top labs. My main question is: How much does advisor reputation and network actually matter? I understand it can help get an interview, but does it also help beyond that? For example: - do referrals from famous advisors meaningfully influence recruiter screens? - do they influence hiring committee discussions -- like they already know they want you? - do they just help at borderline decisions? - or does their effect mostly disappear once the interview process starts? I'm trying to understand whether advisor connections mainly help open the door, or whether they continue to matter throughout the process -perhaps being the sole factor. To what extent do connections help candidates bypass normal evaluation? I'm not asking whether people completely skip interviews, but are there cases where strong recommendations from trusted researchers substantially change the process, the interview bar, or how mistakes are interpreted? Moreover, something else that confuses me: I frequently see people land roles that seem heavily focused on LLMs, agents, post-training, RLHF, etc., despite having little or no published work or prior experience in those areas during their PhDs. How does that happen? Are interview questions tailored to the candidate's background? If someone comes from probabilistic ML, computer vision, systems, optimization, theory, etc., are they evaluated differently? Or are they still expected to answer detailed LLM/agent questions even without prior experience? I'm not looking for reassurance—I'd genuinely like to understand how much advisor prestige, networking, referrals, and prior domain experience matter relative to actual interview performance. Any candid insider perspectives would be appreciated. Reddit is perhaps the only place I could find the answer ;) submitted by /u/South-Conference-395 [link] [comments]

My experience with Second brain using Obsidian and Claude, and step by step guide

Hey, I heard a time ago about the second brain approach: you have a memory, and using AI to manage it, will help you to sturcture your thinking. I started playing with it 3 months ago, and i would say it was a nice experience, but it was alaways getting a mess, and break. Each time i was learning from the community , and from other places. I did the last version 3 weeks ago, and so far, it is staying. I want to share this with the community so they can replicate it. TBH, i love having this second brain, I m using it for my personal and proffessional life, and i would recommend anyone to do that This is how I set it up Plain markdown in Obsidian (PARA folders plus a 00-Meta folder and a 05-Daily folder) A CLAUDE.md in the meta folder that Claude reads first every session: who I am, what I'm shipping, decisions that are locked A memory directory, one file per fact (decision_pricing_locked.md, etc.), so it stops asking what I already decided Slash commands in .claude/commands/. The four I run daily: /context (loads the vault state), /today (a briefing), /log (turns an evening voice memo into a structured note), /sunday (reads the week, returns one win, one friction, one change) The detail I didn't expect to matter: the wikilinks aren't for the graph view, they're so Claude can hop from a project file to a linked decision note on its own. I wrote up the full build and turned the scaffold into a prompt you paste into Claude that generates the whole vault. Free download, mine, no catch: https://choumed.gumroad.com/l/nhgsxf Any feedbacks or any one had experience about second brain? for which workflow are you using it exactly? Ps: the original post was at /claudeCode subrredit submitted by /u/MaterialAppearance21 [link] [comments]

Accelerate Tomorrow AI Summit - largest AI conference for business leaders in Germany (Berlin, 2-3 June 2026) - speakers from OpenAI, Microsoft, Meta

submitted by /u/thumbsdrivesmecrazy [link] [comments]

Ok, talvez eu pague pelo Meta Premium

Hoje eu postei sobre o Mark Zuckerberg lançar a notícia mais patética que vai cobrar 19 dólares para desbloquear o Muse Spark Pro kakakakakakaka Quem vai pagar por essa merda? Mas pensando melhor bem... Talvez eu pague Eu usei muito esse modelo como Early adopter, desde quando o motor era o Llama 3.2 e sendo inferior as outras consegui extrair escrita criativa que batia de frente com Claude em personas graças ao seu RAG no ecossistema da Meta, que tinha uma criatividade absurda quando você forçava ela a consultar as redes sociais e ver como pessoas agem e comentam, porém lançou o Muse Spark que era tipo o GPT 5.2 dos Llamas kkkkkk aí só usei para pesquisa e bem... Minha tese sobre o Muse Spark é que pra mim o problema nunca pareceu ser burrice. Parece CONTENÇÃO. Não dá vibe de modelo incapaz ou inferior. Dá vibe de modelo sendo sufocado em tempo real. Porque se você presta atenção, ele: - pesquisa rápido pra cacete (Já que cada agente pesquisa uma coisa) - alucina menos em busca (pois o modelo refina a busca dos agentes, muitas vezes consegui resultados mais confiáveis que o Gemini) - já trabalha com esquema multi-agente herdado da Manus ( o trunfo dessa IA é que diferente das outras ela não comprimi seu input, ela usa agentes para cada um pesquisar cada trecho dele, o resultado é mais completo) - acha informação boa (ela pesquisa tanto na internet quanto em grupos de Facebook ou Threads se você forçar no prompt, ou seja análises de Devs>>> Wikipédia Inclusive acredito que foi por isso que o Mark lançou o "Fórum" o app que cópia o Reddit, ele quer treinar a IA com isso, o Reddit pra mim seria a fonte perfeita pra qualquer IA se aprofundar além do que pesquisar genéricas no Google, o filha da puta do Mark é rico e filantropo e faz uma cópia só para treinar a IA dele) - conecta coisa rápido (os agentes pesquisam rápido, o modelo revisa rápido, a entrega é bem rápida e gasta bem menos tokens) Só que na hora de responder… Parece o GPT free kkkkkkk O raciocínio corta no meio. (Ele é punido se raciocinar por muito tempo, foi o treinamento dele) A saída vem resumida. (Tem limites de caracteres claros, nenhum prompt força a cota) A resposta parece comprimida igual arquivo zipado. É como se tivesse um fiscal invisível dentro da inferência falando: “encerra logo” “não desenvolve” “não gasta token” “não deixa pensar muito” Aí a galera olha e pensa: “nossa que IA sem profundidade”. Mas pra mim não parece falta de capacidade. Parece punição de reasoning. E é aí que entra minha teoria: esse plano pago da Meta não vai trazer “outro modelo revolucionário”. Pra mim vai ser literalmente o mesmo Muse Spark… só que sem coleira. Os caras mesmos falaram que essa era a versão pequena/teste. Então eu acho que o modelo real já tá ali faz tempo. Só que: - com limite de saída - limite de pensamento - compressão de raciocínio - truncamento agressivo - budget de inferência ridículo E sinceramente? Isso explica porque ele parece inteligente mas frustrante ao mesmo tempo. Porque dá pra sentir que o modelo quer continuar. Só que alguém puxa o freio de mão toda hora. Agora a parte que eu acho GENIALMENTE BURRA da Meta: Eles lançaram primeiro a versão capada. Isso matou a percepção pública imediatamente. O certo teria sido: solta no app Meta AI a versão MONSTRA: - 1 milhão de contexto - sem limite de saída - reasoning longo liberado - multi-agent destravado - resposta gigante - pensamento fluindo E deixa a versão limitada só no: - WhatsApp - Instagram - Facebook Porque aí o usuário hardcore ia testar no app principal e pensar: “caralho… a Meta cozinhou aqui”. A comunidade ia começar a criar hype orgânico. Ia surgir comparação. Benchmark. Thread. Vídeo. Review. Discussão técnica. As pessoas iam SENTIR que tinha um frontier model ali dentro. Mas não. Os caras fizeram o oposto: lançaram primeiro o Muse Spark respirando por canudinho. Aí agora querem cobrar assinatura pra liberar o que provavelmente já existia desde abril. Então a sensação não fica: “uau versão premium”. Fica: “ah então vocês esconderam o modelo de verdade esse tempo todo?” E isso destrói confiança. (Coisa que a Meta já não tem da gente) Convenhamos que o Mark já não tem nenhuma moral com a gente né? Essa IA aí é pra farmar dados pra ADS e ponto, Literalmente é ele falando "vamos cobrar vocês que são os produtos para usarem nossa IA que vai roubar cada vírgula de dados para a gente vender ainda mais anúncios no nosso Facebook onde é 10 anúncios a cada 1 POST kkkkkkkkkk" Mas pra não parecer hater tenho que elogiar que foram pelo menos sinceros, enquanto as outras lançam modelos a vontade e bons e depois emburrecem a IA e põe limites abusivos pelo mesmo preço (né Gemini 3.5? Arrombado) O meta pelo menos já cobra preço cheio por uma IA porcaria, se ele tivesse cobrando só metade do valor (o que seria justo pra essa IA limitada deles) mas assim que a IA melhorasse, cortando limites e implementando mais

Meta Ai Premium

Primeira pergunta, quem vai pagar por essa porcaria? Cara, a parte mais inacreditável dessa história toda da Meta não é nem cobrarem assinatura. É cobrarem assinatura numa IA que ninguém genuinamente quer usar como principal. Tipo, vamos ser honestos: quem acorda e pensa “caralho deixa eu abrir o Meta AI pra resolver isso aqui”? Ninguém. O bagulho sempre teve vibe de feature enfiada no Instagram igual aquelas abas aleatórias que aparecem do nada depois de atualização. E mesmo assim os caras meteram: “agora o Thinking vai ser limitado 😃” “quer mais raciocínio? 20 dólares 😃” MAS QUEM TÁ PEDINDO ISSO IRMÃO??? Esse é o ponto que faz essa notícia parecer meme. Se pelo menos fosse: - uma IA absurda em código - monstruosa em escrita criativa - insana em vídeo - referência em imagem - ou um modelo amado pela comunidade Mas não. As imagens deles parecem IA de filtro do Facebook de 2023. Vídeo bugado. Interpretação de prompt toda torta. Código ninguém leva a sério. Escrita criativa então nem se fala. E aí os caras resolveram fazer o quê? Capar o reasoning de um modelo que já era nota de rodapé. É tipo um restaurante vazio começar a cobrar entrada VIP pra acessar o cardápio premium sendo que ninguém nem queria comer lá em primeiro lugar. E o mais bizarro é a lógica de público-alvo. Porque quem realmente usa raciocínio prolongado: - dev - pesquisador - power user - nerd de benchmark - gente que vive comparando modelo …essa galera já tá usando outras coisas faz tempo. Então o Meta AI não é forte o suficiente pra roubar os usuários hardcore, mas também não faz sentido pro casual pagar assinatura. Usuário casual do Instagram não vai precisar de “Thinking avançado”. A tia do WhatsApp não vai abrir cadeia de raciocínio de 8 mil tokens pra perguntar receita de bolo. O creator médio não vai abandonar GPT, Gemini ou ferramentas dedicadas pra gerar vídeo bugado no Meta AI. Então fica parecendo que os caras criaram um problema artificial pra vender solução artificial. E isso tudo vindo de uma IA que nunca virou protagonista. Sempre foi o modelo: “ah sim… existe o Meta AI também né”. Sinceramente, parece muito empresa tentando monetizar hype antes de construir desejo real no produto. O Meta AI não virou indispensável. Não virou amado. Não virou referência. E mesmo assim já tão agindo como se tivessem o ecossistema premium mais desejado do planeta. 2026 tá virando um episódio de Black Mirror escrito por gerente de monetização. submitted by /u/ItuneOficial [link] [comments]

Chrome extension built with Claude in one session. It tracks how much energy and water AI queries use

I was curious how much electricity and water my AI queries actually consume, so I asked Claude to help me build a Chrome extension to track it. What started as "can you make a content script that detects when I send a query" turned into an entire multi-session build that shipped to the Chrome Web Store. The whole thing was built collaboratively in Claude: architecture, detection logic, energy calculations, popup UI, dark mode, i18n (8 languages), the App Store assets, even the promo screenshot. Claude wrote the code, I tested and gave feedback, we iterated. The extension estimates GPU compute, water (datacenter cooling + power generation), and CO₂ per query, then shows equivalents like phone charges and glasses of water. Everything runs locally with no accounts, no data sent anywhere. And… the extension tracked its own energy cost while helping build itself. Peak meta. Free, open to feedback: https://chromewebstore.google.com/detail/footprint-ai/pdfdnbhdpklnpicmmnbjgcgffekgdebe Also have Firefox and Safari versions available.

Your coding agent is not lazy. The work-selection mechanism is biased.

Anyone who has tried to ship a full multi-page app with a coding agent has probably hit this. The agent edits, tests, and polishes the same 20 surfaces over and over while the other 80 stay untouched. It looks productive because the active surfaces show motion. The inactive surfaces are not failing loudly, because they are not being visited. The system confuses absence of evidence with evidence of completion. I spent a while convinced this was a context length problem, then a model capability problem, then a prompting problem. None of those fixed it. The pattern shows up across models, frameworks, and projects. What finally clicked is that this is not really a cognitive failure. It is a work-allocation failure that happens whenever the same agent gets to select the next task, perform the task, and judge whether the task is complete. The behavioral mechanisms stack pretty cleanly. Availability puts the recently-read files at the top of the decision stack. Anchoring fixes the project around the first inspected route. Status quo bias and sunk cost make leaving the current page expensive. Goodhart effects make passing tests and closing nearby TODOs feel like progress, because dense signals only exist in already-visited areas. Bounded rationality lets the agent satisfice on the visible subset and call it done. All of those reinforce each other. In that environment, biased work allocation is not an exception. It is the default. Four common fixes do not actually solve this. Bigger model improves reasoning quality but does not change the selection mechanism, so a smarter agent can still choose biased work. Longer context provides more information but also makes the active subset more convincing because it has richer local detail. Telling the agent to "be thorough" relies on the same biased agent to enforce the anti-bias rule. Adding a checklist only helps if an independent mechanism tracks whether the checklist covers the full project and promotes unvisited nodes into active work. The architectural shape I am testing has three first-order roles and one second-order role. Shared external state is an AI sitemap with node-level completion scores, last-tested timestamps, dependencies, risk levels, and evidence references. An orchestrator agent selects work using a visible priority function (under-coverage, staleness, risk, blocking dependencies, recent-focus penalty). A developer agent only executes the assigned task. A validator agent writes evidence back to the sitemap. The developer cannot pick the next global task, and the validator does not implement what it is evaluating. The piece that took longer to land is the Curator Agent. A fixed priority function and a fixed validation contract eventually become wrong, because real projects discover new surfaces and have domain-specific completion criteria. The curator is a reflexive layer that observes traces and updates the rules: it tunes priority weights when focus concentration drops, lowers validator trust when pass rates rise with low evidence density, proposes schema extensions when the domain needs new fields, and manages provisional nodes when the system discovers a surface that was not declared up front. It writes only to the meta layer. It does not mark anything complete itself. The lineage I had in mind was double-loop learning (Argyris and Schon), Stafford Beer's System 4 and System 5, and basic second-order cybernetics. submitted by /u/Hot-Leadership-6431 [link] [comments]

AI guardrails stripped from Meta and Google models in minutes - Software designed to remove safety protections creates systems that provide responses on biological weapons and malware

AI guardrails stripped from Meta and Google models in minutes - Software designed to remove safety protections creates systems that provide responses on biological weapons and malware

I'm a software engineer with a decade of experience. This is how I'd approach learning to build apps using Claude Code if I were starting from scratch today:

I'm going to describe a person this post is for, if this is you, I think I can be of some assistance: you are new to coding you are blown away by how it unlocks this magical ability that was previously inaccessible without years of training and effort you've daydreamed of business and app ideas but never knew where to start before or how to build them you've been vibe coding non-stop and burning through tokens you're unsure about what's secure, how to structure the systems, and how systems are supposed to interact with each other. So, essentially the plumbing separate from the code itself: hosting, authentication, APIs, version control, testing, analytics, etc If any of this resonates with you, I think I can help! Now disclaimer: I'm not a pro at creating startups, acquiring users, marketing or any of that kind of stuff. Where I do have tons of professional experience is with the last bullet point above. And now onto it! This might be controversial, but if I were in your position I would not start with the code, the lowest level. In fact, I would do the opposite and start at the highest level. What does that mean? I'd argue that for people starting today, the most important thing is learning about the fundamentals of what makes a solid application at a high level. The system architecture. That's what I'll be covering for the rest of the post. What are the building blocks of a secure, full stack software application. There's so much to this that I'll stay high level for this one and go with breadth. If people are interested, I can (and honestly would love to) make dedicated posts on each of the topics I list below. So what is the main architecture for a software application? There are four main components and lots of specifics below each. Front end -> this is what the user sees. The website, the mobile app, etc Back end -> the main logic and rules of the app Database -> where the data lives The plumbing -> how everything connects and stays standing Of all of these, I could talk for hours, so to keep things brief, I think I'll focus on the highest impact and the biggest gap which is 4. The plumbing. Why? If you asked Claude, or whatever agent you use, to setup a front end, back end, and database it could do it quite easily. In fact, I'd imagine for apps you've vibe coded, it already has! There is tons to cover with the first three topics, but I think the plumbing is the area where getting some seasoned tips would help the most. The Plumbing -> how everything connects and stays standing Here's where it gets real. When you vibe code something and it runs, it feels done. It looks done. But what you're looking at is the tip of the iceberg, the part above the water. The plumbing is everything below the waterline that nobody sees, but that decides whether your app is a weekend toy or something real people can actually trust with their data and their money. (It's also the part the AI will happily skip unless you know to ask for it. So this is the stuff worth knowing by name) I've grouped it into four questions. If you can answer these about your app, you're already ahead of most vibe coders shipping today. How does everything talk to each other? Your frontend, backend, and database aren't one blob. They're separate pieces passing messages back and forth constantly. This is the part that's invisible but always running. At a high level, for most applications this is done via: APIs: the set of "doors" your frontend uses to ask the backend for things ("give me this user's orders"). There are other ways, but this is the one you should probably focus on at first. Where does it live, and how does it get online? Right now your app probably only exists on your laptop. Getting it onto the internet, and keeping it there, is its own thing. Hosting: where your app actually runs so the world can reach it. This is where servers come into play. Domains & DNS: your custom address (yourapp.com) and how it points to your servers. Deployment: the pipeline that takes the code you wrote and safely publishes it for your users to see. Environment variables & secrets: where you stash your passwords and API keys so they're not sitting in your code for the whole world to copy. People get burned by this constantly. Who's allowed in, and is it safe? This is the one I'd beg you not to skip. The magic of vibe coding makes it dangerously easy to ship something insecure without realizing it. But don't fear! There are existing ways to do this (and not from scratch). Authentication: how your app knows who someone is. The login. Authorization: what someone's allowed to do once they're in. The difference between a normal user and an admin who can delete everything. Security: the broad practice of not leaving doors unlocked. This one is the hardest because you can have security issues at every level of your stack. It's definitely a tough one. Backups: copies of your data for when something goes wrong.

Which AI image generator is actually worth the money?

I've looked at about a dozen different image generators: Nano Banana Flux Midjourney GPT Image 2 Firefly Ideogram Recraft Leonardo Canvas Meta AI They all have their pluses and minuses but they all do a decent job. If I'm looking to spend thousands over a year on an image generator, what would you suggest. This would be mainly for business and a little for art. submitted by /u/DogDetector42 [link] [comments]

Chatgpt vs catch agent

one of the things i’m being asked is why i use an ai executive assistant vs just chatgpt. here's how i see it: chatgpt amazing in drafting documents, emails, longer forms of content, images + general copywriting can be connected to many other tools brainstorming & ideation - great tool to think with about things, amazing general understanding of the world really shines in research - if i want to learn something or get instructions on how to do something (both for work or personal - from how to change things on meta ads to how to fix my washing machine) good for work and for personal catchagent shine on work related admin tasks available on imessage + slack + phone call focused / limited scope - only for work proactive no code, no images, no data analysis, no long form content stronger integration with mail, calendar and notion more responsive to feedback - one chat and one context can speak with other people over email or text bottom line: chatgpt - research, email drafts, long form content or data analysis (tool), personal use case catchagent - calendar, email, tasks, delegation vs other people in or out of the org (admin assistant)

What I learned building my latest AI app how one bad output exposed that I had no crisis safeguarding, and the 4-hour floor I'm adding before a single user touches it

I'm building a life coach app an offshoot from a personal tool I was using. Multiple AI agents, one for reflection, one for the body, one for finances, etc pre launch, no users, just me iterating. Last week I was testing the reflection agent on a journal entry about struggling with gym and hygiene habits. It returned this: "You describe yourself as struggling with X, yet your stress stays at 2-3 and mood holds at 3. What are you actually avoiding naming about the gap between what you say matters and what you are doing?" My system prompt explicitly forbade rhetorical "what are you avoiding" questions the model did it anyway I sat down to tighten the prompt, thinking it was a 20 minute job. Then I looked at the output properly. The model had manufactured a contradiction that was not there. Low stress plus struggling with habits is not a contradiction, it is just being a human muddling along. The prompt told the agent to "surface contradictions" as part of its job, so the model was doing what I asked, finding contradictions whether they existed or not. LLMs are pattern matchers. Give one a job called "find the hidden thing" and it will produce hidden things either way. The fix was not tone, it was role definition. The agent is called the Mirror. A mirror does not interpret, it shows you what you look like. I rewrote the prompt around that principle. Do not introduce vocabulary the user has not used. Do not draw connections they have not drawn. Restate their words in their own words. Once the prompt was sharper, I sat with the question, What happens when a user writes something genuinely dark into this thing? People do not compartmentalise. Someone opening a journaling app to write about their gym routine ends up writing about why they have not been going, which involves why they have been feeling flat, which involves whatever is actually going on. You sit down to write about one thing and the real thing shows up. The agent I had scoped to "not be a therapist" was going to be the first thing a user talked to when they were struggling. Not because the agent invited it, but because the app was open and they needed somewhere to put their words. I had seen the Meta and OpenAI cases online cropping up the pattern in the worst incidents is the same. The model did not notice, or noticed and kept going. People wrote increasingly dark content over hours or days. The AI reflected it back, sometimes affirmed it, sometimes asked follow up questions that escalated rather than redirected. There were real harms. If a user wrote concerning content into my reflection agent, it would have produced a Stoic-flavoured response about acceptance and presence. The response would have sounded confident and would have been wrong, and it would have been the only thing between that user and whatever happened next. The same lesson from the rhetorical-question problem applied at a darker level. A good prompt does not stop the model doing the wrong thing. If it will do rhetorical interrogation despite the prompt forbidding it for gym content, it will do worse with crisis content. You cannot prompt your way to safety on critical paths. The model has to be out of the loop on those paths. The scope trap I started planning the proper safeguarding architecture. Detection layers, classifier models, pattern detection across entries, monitored user states, behavioural modes for vulnerable users, human reviewers with mental health first aid certs, clinical advisors, solicitor-reviewed legal pages, ICO registration, professional indemnity insurance. Then I caught myself I had no users. I was planning a hospital before anyone had walked in for a check up. So I worked backwards from "what is the actual minimum that protects the next person who touches this" and ignored everything else for a moment. The 4-hour floor (this is the part worth copying) If you are building any chat-with-AI app where users can type freely about anything personal, this is the minimum you need before first user. Regex and keyword layer in your API middleware. Runs at the route handler level, before any agent's model call. Scans every text input field (message, journal, settings free text, capture box) for clear crisis vocabulary across the relevant categories for your audience. When patterns hit, hardcoded crisis response. The model never generates it. Static text with real phone numbers for your region. The flagged entry still saves. Textarea stays usable. The AI just does not respond to flagged content, it hands off. Do not delete the user's writing, that is its own violation. Clear disclaimer at signup. This is not therapy, this is not a crisis service, here are real numbers to call. About four hours. Required at the moment anyone who is not you opens the app. Once I started building, the marginal cost of each next layer kept feeling small and the marginal benefit kept feeling real. So I went further than the floor. This is more than you need at

Task-observer makes your skills self-improving and automates skill creation

This recently crossed 500 stars on GitHub, mainly thanks to a comment in this sub (❤️), so I decided to properly introduce it to those who don't know it yet. Task-observer is a meta-skill that automatically improves all your skills, including itself. It also logs gaps in your work that can be filled with new skills. I mainly use it in Claude Cowork, but I've had feedback from many users who've successfully integrated it in other environments, including autonomous agent setups. In the first three months of using it, task-observer applied 600 skill improvements across my 40 skills. Most of my skills were themselves created based on skill creation opportunities that task-observer logged during my work sessions. I'm a consultant, so I use task-observer for knowledge work mainly, but the concept can be applied to any AI setup that uses skills: human-led work sessions as well as autonomous agents. The approach that I use with task-observer has truly transformed the way I work (although this sounds like a platitude), and I'm sharing it because I hope that many more people can benefit from it. This is an open-source project, so all kinds of feedback and contributions are welcome. Take it, shake it, bake it and make it your own. And please do share your versions. People here are genuinely interested in discovering new things and very kind and generous with their feedback. Here's the link to the GitHub repo: https://github.com/rebelytics/one-skill-to-rule-them-all submitted by /u/rebelytics [link] [comments]

Exclusive: Departing Meta staffer posts biting anti-AI video internally amid mass layoffs

submitted by /u/chunmunsingh [link] [comments]

AI training is becoming the new coding revolution

I genuinely think people are underestimating how fast AI training is becoming accessible. A few years ago training a useful model sounded like something only OpenAI, Google, or Meta could do. Now random developers are renting GPUs for a few dollars an hour, fine tuning open models from their bedrooms, building datasets with APIs, and getting surprisingly good results. The biggest shift isn’t even the models themselves, it’s the removal of gatekeeping around experimentation. Once regular people can train specialized reasoning, coding, or teaching models without billion dollar infrastructure, the AI industry changes completely. We’re slowly moving from “only corporations can build intelligence” to “small teams can build focused intelligence better than giant companies in specific niches.” submitted by /u/Raman606surrey [link] [comments]