Ahmad D. @padryalw

🛠️ Solo builder | Building in public 🚀 Shipping SaaS one commit at a time. Currently building 👇 https://t.co/0Xs92Jd29L · https://t.co/wlxRy4PEoR readsira.com Düsseldorf Joined December 2010

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MAX RAHN @aktien_max

3 days ago

Innovation entsteht dort, wo man Dinge ausprobieren darf. USA: darf. China: darf. EU: Der Antrag auf Innovation wird seit 3 Jahren geprüft und in 17 Gremien diskutiert.

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Sera Aurelia X @SeraAureliaX

4 days ago

🤣🤣🤣

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Akshay 🚀 @akshay_pachaar

4 months ago

x.com/i/article/2029…

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LLM fine-tuning techniques I'd learn if I were to customize them: Bookmark this. 1. LoRA 2. QLoRA 3. Prefix Tuning 4. Adapter Tuning 5. Instruction Tuning 6. P-Tuning 7. BitFit 8. Soft Prompts 9. RLHF 10. RLAIF 11. DPO (Direct Preference Optimization) 12. GRPO (Group Relative Policy Optimization) 13. RLAIF (RL with AI Feedback) 14. Multi-Task Fine-Tuning 15. Federated Fine-Tuning Since we're talking about fine-tuning, I wrote a full breakdown on fine-tuning LLMs with RL in 2026. Including how to skip manual reward engineering with automatic LLM-graded rewards. And this is done using a 100% open-source solution: github.com/openpipe/art The article is quoted below.

Akshay 🚀 @akshay_pachaar

4 months ago

x.com/i/article/2029…

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Globe Eye News @GlobeEyeNews

2 weeks ago

Germany’s Leipzig suspends all tram services due to extreme heat.

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Joe Ingeno @JoeIngeno

2 weeks ago

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Ahmad D. @padryalw

2 weeks ago

Not every business needs thousands of subscribers. This quarter: • €26 MRR • €4,555 payment volume I’d rather solve expensive problems for a few clients than cheap problems for thousands.

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Dan Kornas @DanKornas

2 weeks ago

Stop learning LLM internals from random one-off tutorials. LLM Internals is a step-by-step GitHub learning repo for understanding how large language models work under the hood. It helps you build a cleaner mental model by organizing blogs and videos from tokenization to attention math, Transformer components, training concepts, and inference optimization. Key features: • Fundamentals first – starts with LLMs, RAG, MCP, agents, fine-tuning, quantization, tokenization, and BPE • Attention math – walks through Q/K/V, √dₖ scaling, causal masking, RoPE, and grouped-query attention • Transformer components – covers the architecture, feed-forward networks, normalization, MoE, and LoRA • Training concepts – includes backpropagation, cross-entropy loss, RLHF, and reasoning models • Inference optimization – covers KV cache, paged attention, Flash Attention, speculative decoding, continuous batching, and prompt caching It’s open-source (Apache License 2.0). Link in the reply 👇

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Ahmad D. @padryalw

2 weeks ago

@sadanand_dev Love to connect as well

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Ahmad D. @padryalw

2 weeks ago

if you're building in public, let's connect. i'm a solo dev shipping two products and i'd love to follow more people doing the same. drop your @ and what you're working on.

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Ahmad D. @padryalw

2 weeks ago

been quietly building two products on my own for a while now. no big launch, no hype, just showing up most days and shipping something small. looking back at the last few months, the progress kind of sneaks up on you. small stuff really does add up.

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Vivek Galatage @vivekgalatage

4 weeks ago

Introduction to Parallel Algorithms cs.cmu.edu/~guyb/paralg/p…

Vivek Galatage @vivekgalatage

a month ago

One of the best free courses on low-level performance programming - Aalto University's Programming Parallel Computers covers SIMD, pipelining, cache optimization, and more. If you care about squeezing CPU cycles, this is definitely worth taking up. ppc.cs.aalto.fi

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tut_ml @tut_ml

a month ago

Learn EDA- mltut.com/top-coursera-d…

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Angular @angular

a month ago

Angular v22 is now available 🥳 Here is a quick look at what's landing today: ✅ Signal Forms, Angular Aria & resource are officially STABLE ✅ OnPush change detection is now the default ✅ Template updates and more Read the full breakdown on the blog: 📰 goo.gle/angular-v22-bl… Which of these updates is your favorite?

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Alex Xu @alexxubyte

2 months ago

What is Google’s TPU? A TPU (Tensor Processing Unit) is Google’s custom AI chip, designed from scratch for the giant matrix multiplications that modern models live on. GPUs were built for graphics first. TPUs were built for deep learning from day one. At Cloud Next ’26, Google unveiled its 8th generation, and for the first time it ships in two flavors. TPU 8t is built for training, where raw throughput wins. TPU 8i is built for inference, where latency and chip-to-chip speed matter most. Both still share the same Axion CPUs, liquid cooling, and software stack, so code written for one runs on the other. The diagram below is a quick study guide to what’s the same, what’s different, and why, based on our understanding of published Google articles.

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Vivek Galatage @vivekgalatage

2 months ago

Algorithms by Jeff Erickson - one of the best algorithm books out there. The illustrations make complex concepts surprisingly easy to follow. Highly recommend this. jeffe.cs.illinois.edu/teaching/algor…

Vivek Galatage @vivekgalatage

3 months ago

My go-to algorithm visualizer for explaining DSA to students. algorithm-visualizer.org

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Dhanian 🗯️ @e_opore

3 months ago

System Design Roadmap │ ├── 1. Introduction to System Design │ ├── What system design is │ ├── Importance in real-world applications │ └── Key goals: scalability, reliability, performance │ ├── 2. Fundamental Concepts │ ├── Networking basics (HTTP, HTTPS, DNS) │ ├── Client-server architecture │ └── Latency vs throughput │ ├── 3. Databases & Storage │ ├── SQL vs NoSQL │ ├── Data modeling │ ├── Indexing │ └── Replication and sharding │ ├── 4. Scalability Principles │ ├── Vertical scaling │ ├── Horizontal scaling │ └── Load distribution │ ├── 5. System Components │ ├── Load balancers │ ├── Application servers │ ├── Databases │ ├── Cache (Redis, Memcached) │ └── CDN │ ├── 6. Caching Strategies │ ├── Client-side caching │ ├── Server-side caching │ └── Cache invalidation │ ├── 7. Distributed Systems │ ├── CAP theorem │ ├── Consistency models │ ├── Fault tolerance │ └── Consensus basics │ ├── 8. API Design │ ├── RESTful APIs │ ├── Rate limiting │ └── Authentication & authorization │ ├── 9. Message Queues & Async Systems │ ├── Event-driven architecture │ ├── Queues (Kafka, RabbitMQ) │ └── Background processing │ ├── 10. Designing Real Systems │ ├── URL shortener │ ├── Chat system │ ├── File storage system │ └── News feed system │ ├── 11. Monitoring & Reliability │ ├── Logging │ ├── Metrics and alerts │ └── High availability │ └── 12. Interview Strategy ├── Clarify requirements ├── High-level design first ├── Deep dive into components └── Discuss trade-offs →Grab the System Design Roadmap Handbook → codewithdhanian.gumroad.com/l/urcjee