标签：#AR

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You don't have a million labeled images or a GPU farm — and you don't need them. Transfer learning lets you stand on a model someone else trained and reach high accuracy with a few examples in minutes. Here's the idea, visualized. ♻️ Race scratch vs transfer: https://dev48v.infy.uk/dl/day17-transfer-learning.html The insight The early layers of a trained network learn general features — edges, textures, shapes — that are useful for almost any vision task. Only the last layers are task-specific. So why relearn edges from scratch? Two ways to do it Feature extraction: freeze the pretrained backbone, replace the final classifier with a small new "head," and train only the head on your data. Fast, needs little data. Fine-tuning: also unfreeze the top few backbone layers and train them at a low learning rate so you adapt without wrecking what they learned. The demo races two accuracy curves: "from scratch" crawls up and plateaus low (not enough data); "transfer learning" starts high and climbs fast. Tweak the example count and freeze/fine-tune to see them respond. Why it matters now This is exactly why fine-tuning an open LLM works: a foundation model already learned language; you adapt it cheaply. Transfer learning is what makes deep learning practical for the rest of us. 🔨 Full recipe (load pretrained → freeze → new head → train → optionally fine-tune low-LR) on the page: https://dev48v.infy.uk/dl/day17-transfer-learning.html Part of DeepLearningFromZero. 🌐 https://dev48v.infy.uk

Deixa eu adivinhar. Você tá com um projeto Laravel rodando, tem uns 5, 10, talvez 15 formulários que recebem CPF. Cadastro de cliente, cadastro de fornecedor, atualização de perfil, checkout, área administrativa… e em cada um desses lugares tem aquela mesma lógica de validação de CPF. Copiada. Colada. Com pequenas variações. E tá tudo bem. Até o dia em que o cliente pede pra mudar uma regra. Ou um bug aparece em um formulário e funciona normal no outro. Aí você abre o projeto, dá um Ctrl+Shift+F procurando "cpf" e… surpresa: tem oito lugares diferentes com a mesma validação. Com mensagens de erro escritas de oito jeitos. Uma delas até com erro de digitação. Já passou por isso? Então senta que essa conversa é pra você. O crime acontecendo em câmera lenta Olha esse cenário aqui, que eu garanto que você já viu (ou escreveu): // app/Http/Requests/StoreClienteRequest.php public function rules () { return [ 'cpf' => [ 'required' , function ( $attribute , $value , $fail ) { $cpf = preg_replace ( '/[^0-9]/' , '' , $value ); if ( strlen ( $cpf ) !== 11 ) { $fail ( 'CPF inválido.' ); return ; } // ... mais 20 linhas do algoritmo }], ]; } E aí, três dias depois, no outro Form Request: // app/Http/Requests/StoreFornecedorRequest.php public function rules () { return [ 'cpf' => [ 'required' , function ( $attribute , $value , $fail ) { $cpf = preg_replace ( '/[^0-9]/' , '' , $value ); if ( strlen ( $cpf ) !== 11 ) { $fail ( 'O CPF informado não é válido!' ); // mensagem diferente, claro return ; } // ... mais 20 linhas quase iguais, mas não exatamente }], ]; } Multiplica isso por 8 telas. Agora imagina o seu "eu do futuro" tentando manter isso. Dá pra sentir a dor daqui. DRY: a sigla que vai salvar seu projeto (e sua sanidade) DRY significa Don't Repeat Yourself . Em bom português: não se repita, caramba. A ideia é simples: cada pedaço de conhecimento (uma regra de negócio, um cálculo, uma validação) deve existir em um único lugar no seu sistema. Se precisar mudar, você muda em u

While experimenting with GRPO training, I kept running this shit that when reward increases, it becomes difficult to tell whether the policy is genuinely improving or simply exploiting the reward function. So I built a small library called rewardspy that wraps an existing reward function and continuously monitors indicators that often precede reward hacking. It currently tracks things like rolling reward statistics, reward variance collapse, reward component imbalance, response length drift, reward slope changes, GRPO group collapse, anol. This is my first major RL project so I would absolutely love some technical advice Check it out here: https://github.com/AvAdiii/rewardspy (credits to u/Oranoleo12 , posting on their behalf) submitted by /u/BaniyanChor [link] [留言]

This is just an opinion of what I experience and am witnessing, but looking at how LLMs scale feels like I've seen it before: with CPUs trying to outrun Moore's Law and break the rules of physics. Heat, power leakage, and diminishing returns made it increasingly expensive to squeeze out even small gains in clock speed. The GHz race shifted because it had to. For LLMs, more compute, more data, more parameters, and everything just keeps getting better? That curve seems to hit a ceiling and innovation needs to succeed the scaling race now. History does not repeat itself, but it rhymes. What learnings can we make from history to "predict" a potential future? History In the early 2000s, CPUs ran into a wall, a very physical one ^^ So makers adapted. Instead of crunching every single watt out of a single core, multi-cores became common. Athlon 64 x2, Pentium D, PS3 with its heavy Cell approach. From linear to parallel. From sequential to multi-threaded (and funny race conditions ;). Talks of distributed systems, SIMD/MIMD and new benchmarking spawned into what we have today. We still use CPUs, but differently. We still have Memory, but think about Cache, RAM, GPU or Unified. Same same, but different. Innovation because of limitation. Present I feel something similar is about to happen to gen AI. Yes, there are improvements in different areas, some in scaling, some optimisation, some performance, but the slope is becoming slippery. The last 12 months went from "Opus 4.5 is the pinnacle" to "What the hell is wrong with Claude?". The perfect (business) storm of scaling execution! But the low-hanging fruits have been eaten and the crops don't grow as fast anymore. Costs rise quickly, latency becomes a constraint, and even large context windows feel more like extensions than breakthroughs. What remains is more incremental, more expensive, and more complex. You could argue the whole venture of "agents" is the same multi-core experience repeating itself. A different kind of orch

I'll be honest: I almost did multi-tenancy the wrong way. When I started building InspectIQ "a SaaS platform for Florida home inspectors" my first instinct was to add a tenant_id column to every table and filter it in the application layer. Every query would have a WHERE tenant_id = :current_tenant clause. Simple, familiar, done. Then I thought about what happens when you forget one. One missing WHERE clause. One endpoint that skips the filter. One inspector sees another inspector's client data. In a home inspection business, that's not just a bug — it's a HIPAA-adjacent nightmare and a trust-destroying moment with your first customer. So I did it properly from day one: Row Level Security at the database layer. What is Row Level Security? RLS is a PostgreSQL feature that lets you define policies directly on tables. When a user queries a table, the policy runs automatically, before your application code even sees the results. You can't forget to apply it. You can't bypass it with a careless JOIN. It's enforced at the lowest possible layer. For a multi-tenant SaaS, this is exactly what you want. How I implemented it Every table in InspectIQ has this pattern: ALTER TABLE inspections ENABLE ROW LEVEL SECURITY ; ALTER TABLE inspections FORCE ROW LEVEL SECURITY ; CREATE POLICY tenant_isolation ON inspections USING ( tenant_id = NULLIF ( current_setting ( 'app.current_tenant_id' , true ), '' ):: uuid ); The FORCE is important — it applies the policy even to the table owner. No superuser backdoor. The tenant context comes from the JWT. When an inspector logs in, their tenant_id is embedded as a custom Cognito claim. The FastAPI middleware extracts it and sets it at the start of every request: await session . execute ( text ( f " SET LOCAL app.current_tenant_id = ' { tenant_id } '" ) ) SET LOCAL scopes the setting to the current transaction. When the transaction ends, it's gone. No leakage between requests. Aurora PostgreSQL Serverless v2 I'm running this on Aurora PostgreSQ

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