Top stories in tech today: – Big Tech spends $226K a day to lobby Congress
– Meta and Microsoft are slashing thousands of jobs – Tesla is spending $25B to reinvent itself
– Instagram tests a stripped-down Snapchat rival
– Quick hits on other tech news
RESEARCH
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Interesting Recent Open Source Projects: Mirofish, Paperclip, Hermes Agent, AutoResearch
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Deep Learning: ConvNets and ViTs Training with Backpropagation
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Deep learning. Largely ConvNets or ViTs trained with backprop
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Robotic Technology Scaling Requires Years Not Months
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Whilst robotic tech is accelerating fast, I still believe we are looking at years and not months away for successful and safe implementation and scaling. https://t.co/OcKD9pP90n
— AI (@DeepLearn007) 24 avril 2026Whilst robotic tech is accelerating fast, I still believe we are looking at years and not months away for successful and safe implementation and scaling.
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LLaTiSA: Difficulty-Stratified Time Series Reasoning Visual Perception
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LLaTiSA
— AK (@_akhaliq) 24 avril 2026
Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
paper: https://t.co/ODOruVboIu pic.twitter.com/ZM5G3vY3IKLLaTiSA Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics paper: https://
huggingface.co/papers/2604.17
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DeepSeek’s open-source strategy challenges US AI monopolies, alters AGI business models.
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I swear DeepSeek open-sourcing everything is some Sun-Tzu shit. America is trying to build trillion-dollar AI monopolies, and China is trying to make that impossible. If the secret recipe to AGI is (eventually) public, the business model changes. Valuations compress.
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Deep Learning ConvNets: Core AI Beyond Language Models
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The vast majority of them use deep learning, i.e. ConvNets trained with backprop. It's not LLMs but it is AI.
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Deep Learning Without LLMs: Neural Net Backpropagation Focus
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It's all based on neural net / deep learning trained with backprop.
But none of it uses LLMs, GPT, or other token-based generative architectures. -
LLMs vs Vision Systems: Neural Nets and Architecture Differences
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Absolutely nothing. They both use neural nets and backprop. But LLMs are generative architectures trained on sequences of discrete symbols. Vision systems used in AEBS and other applications use ConvNets or ViTs trained to detect and classify from labelled samples.
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Anthropic Accelerates Product Development Timelines with Latest Models
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My biggest takeaways from Claude Code's Head of Product @_catwu
: 1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is