Google is reorganizing its AI coding strike team as it tries to close the gap with Anthropic in one of the most lucrative parts of the AI market. According to The Information, the months-old team is being expanded into a more formal "midtraining" group, sitting between
CODE
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Sazabi raises $8M to fix monitoring gap for AI coding agents
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Nobody saw this gap until the AI-coding-agent wave hit hard:
— Robert Scoble (@Scobleizer) 25 juin 2026
Cursor, Claude Code, and Codex fundamentally changed how software gets written. Nobody rebuilt monitoring for the teams actually using them.
Sazabi just closed $8M to fill that hole. Their platform treats logs as… https://t.co/DWBkEgczcaNobody saw this gap until the AI-coding-agent wave hit hard: Cursor, Claude Code, and Codex fundamentally changed how software gets written. Nobody rebuilt monitoring for the teams actually using them. Sazabi just closed $8M to fill that hole. Their platform treats logs as
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DFlash: Drop-in Speculative Decoding for SGLang, vLLM, TensorRT-LLM
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/7 Drop-in for SGLang, vLLM, and TensorRT-LLM. No code refactoring. SGLang:
–speculative-algorithm DFLASH
–speculative-draft-model-path z-lab/Qwen3-8B-DFlash-b16 vLLM: via the Speculators library (
http://
docs.vllm.ai/projects/specu
lators
…, algorithm "dflash") MIT license. ICML 2026 accepted. -
Comparison of GLM 5.2 and Opus 4.8 on SDPO paper reproduction
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Here’s a fun comparison between GLM 5.2 and Opus 4.8 on a one-shot reproduction of the SDPO paper This is a hard task: the model must resolve messy verl issues and then run ablations to completion and confirm the paper’s claims. – GLM 5.2 costs $6.21 while Opus 4.8 cost us
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DeepReinforce releases Ornith-1.0 self-improving open-source coding model
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DeepReinforce has released Ornith-1.0, their new self-improving family of open-source models designed for agentic coding. > Ornith-1.0 learns to write its own task scaffolds during training rather than relying on human-designed harnesses. > The 397B MoE flagship can match
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Agentic coding requires explicit API contracts and docstrings
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Agentic coding forces you to design clean interfaces and document them well. An agent cannot read the implicit mental model shared by your engineering team, it can only read your API contracts and docstrings.
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Step By Step Guide To Powering Your Application With LLM
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Step By Step Guide To Powering Your Application With LLM! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Guide-to-App-L
LM
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Deep Dive into LSTM and xLSTM
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Deep Dive into LSTM and xLSTM! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Deep-Dive-into
-LSTM
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Platt Scaling for Model Calibration: A Visual Guide
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Platt – Scaling for Model Calibration: A Visual Guide! – John Platt #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming
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Cheatsheets Collection for Neural Networks Part 2
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Cheatsheets! The Complete Collection of Neural Networks! Part 2 @kdnuggets #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode