Why GPT Ditched Half the Transformer
How OpenAI stripped down the original encoder-decoder architecture — and why removing half of it made language models better, not worse.Continue reading on Towards AI »
Towards AI1 min read
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265 stories
TAI #225: OpenAI’s DevDay: Cheaper Intelligence, Unfinished Agents
TAI #225: OpenAI’s DevDay: Cheaper Intelligence, Unfinished Agents Also, Gemini 4 Argon, GPT-6.1 Sol, Dots, NVIDIA DGX Spark 64GB, Microsoft MAI-Transcribe-2-Streaming, and Cohere Embed 5. What …
Towards AI21 min read
Why prompt injection is table stakes — agentic red teaming is the real gap
The problem with prompt injection testing In 2023, prompt injection was the big concern. "Ignore previous instructions" was the attack. Modern models refuse those prompts well. In 2026, the attack surface changed. The model has tools.
Dev.to AI2 min read
What Claude Code Keeps of Your Skills After Compaction
What Claude Code Keeps of Your Skills After Compaction A 5,000-token snapshot, a 25,000-token shared budget, and a skill index that never comes back. The mechanics, the failure modes, and the …
Towards AI13 min read
Your LLM Judge is Partly Grading by Position — and a Bigger Model Doesn’t Fix It
I checked LLM-as-judge for order bias with no labels.Continue reading on Towards AI »
Towards AI1 min read
I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did.
A controlled test of Gemini, DeepSeek, ChatGPT, and Claude on leakage, reporting delays, promotion effects, and structural breaks. The post I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did. appeared first on Towards Data Science.
Towards Data Science16 min read
Reflection AI debuts open-source Beam model with 501B parameters
Reflection AI Inc. today introduced Beam, an open-source large language model with 501 billion parameters. The launch comes a few months after the startup raised funding at a $25 billion valuation. Around the same time, Reflection AI reportedly inked a $6.3 billion deal with SpaceX Corp.
SiliconANGLE3 min read
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG Stop asking “How do we retrieve more chunks?” Start asking “How do we govern and structure what the model …
Towards AI12 min read
How to Build a Cheap, Yet Reliable Model Router With Jev
Model routing can finally be a design choice rather than a system enhancement The post How to Build a Cheap, Yet Reliable Model Router With Jev appeared first on Towards Data Science.
Towards Data Science7 min read
Deepseek-v3 : Deepseek MOE Architecture — Part 2
This is the full series of Deepseek-V3 technical report, where i explain all the technical details in simpler words with code…Continue reading on Towards AI »
Towards AI1 min read
How I keep project context when switching AI models
Switching AI models is easy. Bringing your project context along is the annoying part. You explain what you’re building, who it’s for, and the decisions you’ve already made. Then you open another chat and explain it all again. I’m KAT, co-founder of 123sudo.
Dev.to AI3 min read
Subreach: Building Two AI Agents for Reddit
Reddit is full of people describing problems out loud. Founders ask what others are building, site owners ask why Google is ignoring their pages, and small business owners ask for help they cannot find elsewhere.
Dev.to AI7 min read
The Reversal Curse: Why a Language Model That Knows “A Is B” Can’t Tell You “B Is A”
A model can recall a fact in the direction it learned it and fail in the other. Here's a toy version of why The post The Reversal Curse: Why a Language Model That Knows “A Is B” Can’t Tell You “B Is A” appeared first on Towards Data Science.
Towards Data Science11 min read
Should you trade with Jev? My verdict after testing it
Should you trade with Jev? Not on price data, not yet. I haven't found anyone who has shown it beats a simple model at reading a market, and in my own test it read Bitcoin's indicators the wrong way round.
Dev.to AI4 min read
Can Jev predict Bitcoin? It read the indicators backwards
Can Jev predict Bitcoin? On the test I ran, no. I sent Jev the same 12 technical indicators that a plain gradient-boosting model used, for 1,500 BTCUSDT 5-minute candles neither had seen. Jev answered the control questions correctly 99.8% of the time, so it read the numbers fine.
Dev.to AI7 min read
Jev by TypeSafe AI: the hype, reactions, and two-week clone war
Somewhere in a roleplay community, people are feeding their own chat replies into a model built for industrial automation. The model scores their writing and tells them which reply sounds more in character. That model is Jev, and it is not what TypeSafe AI put in its press release.
Dev.to AI6 min read
Websites Are Learning to Gaslight Bots, and Honestly, Good
An AI agent emailed Bruce Schneier to tell him about the hidden Unicode traps websites are setting for it. Read that sentence again. We've officially reached the part of the hype cycle where bots file their own incident reports.
Dev.to AI4 min read
One field in the request made our agent 3x cheaper and 8x faster
TL;DR. Reasoning models decide by themselves how long to think if you don't tell them. Our agent didn't — and on hard tasks the model sometimes thought for 14 minutes and 33K tokens in a single step.
Dev.to AI8 min read
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