I spent some time digging into LangGraph and Google’s ADK.
If you're building multi-agent systems, then consider reading the article: https://t.co/IZCVmyC3gw
Dear Prime Minister Ambani,
Please accept my humble apologies for not realizing that you are the real boss of India.
Naturally, you would prefer to maintain your monopolistic exploitation of the great people of India, but would you nonetheless consider allowing Starlink to compete?
There are many areas of India, as there are in all countries, with no Internet access, which denies children the opportunity for education and for small businesses the opportunity to sell their goods to a global market. Starlink would change their lives for the better.
Thank you 🙏
OpenClaw just changed the build speed ceiling for AI agents. When teams can go from zero to revenue in days, tool choice is leverage. Grabbing insights from the OpenClaw Buildathon crew. join in: https://t.co/aSu2KZycZt
Hey everyone! I’ve just put together a write-up on prompt engineering. It covers practical tips and straightforward strategies to get the best results from your prompts. Check it out and let me know your thoughts! https://t.co/bFqVNXUnVJ
@VTikke Overall ML caliber will help. Build something that is impact at good level, strengthen ml basics, stay on top of latest developments in this line. Understand the product that you are working on.
DeepSeek just fixed one of AI’s oldest problems
Hyper-Connections added multiple residual paths: great gains, but signal mixing blew up to 3000× across depth.
DeepSeek uses Sinkhorn (1967) to force residual mixing to conserve signal by design.
3000× → ~1.6× and +6.7% cost.
You're in an ML Engineer interview at MistralAI.
The interviewer asks:
"We need an LLM that excels across code, math & creative writing. How do you achieve multi-domain performance?"
You: "I'll increase the number of attention heads."
Interview over.
Here's what you missed:
DeepSeek just dropped a banger paper to wrap up 2025
"mHC: Manifold-Constrained Hyper-Connections"
Hyper-Connections turn the single residual “highway” in transformers into n parallel lanes, and each layer learns how to shuffle and share signal between lanes.
But if each layer can arbitrarily amplify or shrink lanes, the product of those shuffles across depth makes signals/gradients blow up or fade out.
So they force each shuffle to be mass-conserving: a doubly stochastic matrix (nonnegative, every row/column sums to 1). Each layer can only redistribute signal across lanes, not create or destroy it, so the deep skip-path stays stable while features still mix!
with n=4 it adds ~6.7% training time, but cuts final loss by ~0.02, and keeps worst-case backward gain ~1.6 (vs ~3000 without the constraint), with consistent benchmark wins across the board
🚨 WATCH: Ex-Google CEO's BANNED Interview LEAKED: "You Have No Idea What's Coming"
Ex-Google CEO Eric Schmidt recently made headlines with some controversial comments about AI during an interview conduced at Stanford University. This interview was taken down at his request
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Travel period: 1 July to 31 July 2026.
Grab the deal, pack your bags, and let Vietjet take you there.
I was inspired by this so I wanted to see if Claude Code can get into my Lutron home automation system.
- it found my Lutron controllers on the local wifi network
- checked for open ports, connected, got some metadata and identified the devices and their firmware
- searched the internet, found the pdf for my system
- instructed me on what button to press to pair and get the certificates
- it connected to the system and found all the home devices (lights, shades, HVAC temperature control, motion sensors etc.)
- it turned on and off my kitchen lights to check that things are working (lol!)
I am now vibe coding the home automation master command center, the potential is 🔥.And I'm throwing away the crappy, janky, slow Lutron iOS app I've been using so far. Insanely fun :D :D
I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.
Anthropic co-founder, Jack Clark:
by summer 2026, the AI economy may move so fast that people using frontier systems feel like they live in a parallel world to everyone else
most of the real activity will happen invisibly in digital, AI-to-AI spaces, with only surface signs showing up in everyday life (datacenters, compute/power constraints, and the startup ecosystem)