Abhishek Ranjan @abhishek_rp2002

swe. applying machine intelligence to solve practical problems at scale linkedin.com/in/abhishekrp2… bengaluru Joined October 2021

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AI Daily Feed 🕸️ @AIDailyFeed

2 days ago

Grab your coffee and read this blog, Worth reading.

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Abhishek Ranjan @abhishek_rp2002

a day ago

ran into a prompt design problem today, we’ve got a prompt template defined for a custom use case where: - top / starting of the prompt holds the "instructions" - bottom / ending of the prompt holds "examples" (few shot prompting) however the catch was → at times, the two…

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Abhishek Ranjan @abhishek_rp2002

2 days ago

planning to go deeper into understanding how we can execute/solve multi-step, long-horizon tasks efficiently at scale, does anyone have any go-to: - research papers - architectures or system designs - practical resources ..that helped you understand how to design/execute…

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ℏεsam @Hesamation

3 days ago

a senior engineer at google just dropped a 400-page free book on docs for review: agentic design patterns. the table of contents looks like everything you need to know about agents + code: > advanced prompt techniques > multi-agent patterns > tool use and MCP > you name it

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Piotr Mazurek @tugot17

6 days ago

good things coming 🧐

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Renç Korzay @renckorzay

a week ago

OpenAI fired this 23-year-old from their Superalignment team. But he turned his insider knowledge into a $1.5B fund that's outperforming Wall Street by 700% this year. He says maybe ~200 people in SF understand what's *actually* happening in AI right now. Here's his thesis: 🧵

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Jacques.web3 @jacques_web3

2 weeks ago

not FAANG anymore, it's among us now

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Rohan Paul @rohanpaul_ai

2 weeks ago

FAANG software engineer tells how they vibe code at FAANG --- reddit. com/r/vibecoding/comments/1myakhd/how_we_vibe_code_at_a_faang/

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Reads with Ravi @readswithravi

2 weeks ago

THIS:

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Rohan Paul @rohanpaul_ai

2 weeks ago

BRILLIANT Paper. 💡 A small Qwen2.5 model is fine-tuned to think over retrieved documents, so a single lean setup can answer domain questions on resource-constrained local hardware. Using summarised NHS pages, retrieval hits the right condition among top‑5 in 76% of queries,…

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Charles Miller @charlesmiller_7

3 weeks ago

Anxiety happens in the middle

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Greg Brockman @gdb

3 weeks ago

super cool to compare the outputs from GPT-1 through GPT-5, given the same prompt: progress.openai.com

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Kate Deyneka @deyneka_e

3 weeks ago

The fastest way to get smarter is to read smarter people. I went through @tbpn’s Metis List and pulled together blogs from some of the most influential people in AI. I’ve also added a few gems outside the list 🧵 I find the most value in blogs where people share applied…

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Avi Chawla @_avichawla

3 weeks ago

A graph-powered all-in-one RAG system! RAG-Anything is a graph-driven, all-in-one multimodal document processing RAG system built on LightRAG. It supports all content modalities within a single integrated framework. 100% open-source.

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javinpaul @javinpaul

4 weeks ago

10 Microservices Patterns for Experienced Developers 1. Saga - buff.ly/3LIrPKs 2. CQRS - buff.ly/3ShrBNi 3. Database Per Microservices - buff.ly/3yoSFUP 4. Service Discovery - buff.ly/3WvaOZs

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Robert Youssef @rryssf_

a month ago

HuggingFace just dropped 9 free AI courses and they’re insanely good. LLMs, agents, vision, 3D, audio, gaming all hands-on. All open-source. All free. Here’s what’s inside ↓

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Denny Zhou @denny_zhou

a month ago

Slides for my lecture “LLM Reasoning” at Stanford CS 25: dennyzhou.github.io/LLM-Reasoning-… Key points: 1. Reasoning in LLMs simply means generating a sequence of intermediate tokens before producing the final answer. Whether this resembles human reasoning is irrelevant. The crucial…

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Anthropic @AnthropicAI

3 months ago

New on the Anthropic Engineering blog: how we built Claude’s research capabilities using multiple agents working in parallel. We share what worked, what didn't, and the engineering challenges along the way. anthropic.com/engineering/bu…

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Jason Rosenfeld @jrosenfeld13

4 months ago

AGI achieved internally. Using @DSPyOSS and Keras 3, I built a system where an LLM can self-reflect and modify its own keras source code based on iterative performance. See its reasoning as it changes its neural network architecture after an iteration. Open source soon.