Tech

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VibeThinker-3B: A 3B Dense Reasoning Model Built on Qwen2.5-Coder-3B With the Spectrum-to-Signal Post-Training Pipeline

While recent breakthroughs in AI reasoning have largely been driven by massive scale, pouring in billions of parameters to cross complex cognitive thresholds—VibeThinker-3B is charting a completely different path. Created by researchers from Sina Weibo Inc (China), this 3-billion-parameter model proves that efficiency can punch far above its weight class. Released under an open-source MIT […]

VibeThinker-3B: A 3B Dense Reasoning Model Built on Qwen2.5-Coder-3B With the Spectrum-to-Signal Post-Training Pipeline Read More »

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7,000 Langflow servers are under attack. LangGraph and LangChain have the same holes

Your AI agent did exactly what it was designed to do. The framework underneath it just handed an attacker a shell on the box that holds your OpenAI key, your database credentials, and your CRM tokens. That is not a hypothetical. In a few months, three of the most widely deployed AI agent frameworks each

7,000 Langflow servers are under attack. LangGraph and LangChain have the same holes Read More »

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Fine-tuning forgets. RAG leaks context. Hypernetworks build the model your agent needs on demand.

Enterprise teams keep watching the same thing happen. An AI agent demos beautifully, goes to production, and stalls: it runs for a short stretch, then needs a human to top up its context and check its output, and the promised efficiency drains into supervision. The agent did the work; you did the watching. It’s one

Fine-tuning forgets. RAG leaks context. Hypernetworks build the model your agent needs on demand. Read More »

Liquid AI Introduces LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: Dense Bi-Encoder and Late-Interaction Models for Fast Multilingual Search Across 11 Languages

This week, Liquid AI released two new retrieval models. They are LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M. Both hold 350M parameters. Both are the first bidirectional members of the LFM family. They build on LFM2.5-350M-Base, released in March. The pair targets fast multilingual and cross-lingual search across 11 languages. Their footprint is small enough to run almost anywhere.

Liquid AI Introduces LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: Dense Bi-Encoder and Late-Interaction Models for Fast Multilingual Search Across 11 Languages Read More »

Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks

In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen. We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step

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Anthropic’s Claude Code Artifacts update brings live, shared dashboards and interactive workspaces to enterprises

Anthropic announced a potentially game-changing new feature for users of Claude Code on the Claude Team and Enterprise subscription plans: Artifacts. This update turns a Claude Code session’s work into a live, interactive, and shareable, custom HTML webpage, allowing a Claude Code user to plug in live code, multiple data sources, and have it surface

Anthropic’s Claude Code Artifacts update brings live, shared dashboards and interactive workspaces to enterprises Read More »

Perplexity Launches Brain, a Self-Improving Memory System That Builds a Context Graph of an Agent’s Work and Learns Overnight

Most AI memory remembers the user. It stores your preferences, your tastes, and your role. Perplexity is taking a different path. Today, Perplexity launched Brain, a self-improving memory system for its agent product, Computer. Brain does not focus on remembering you. It remembers what the agent did. That reframes what memory in AI is for.

Perplexity Launches Brain, a Self-Improving Memory System That Builds a Context Graph of an Agent’s Work and Learns Overnight Read More »

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New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget

Imagine your engineering team just deployed an AI agent to search through internal company documents and answer employee questions. It works perfectly in development, but in production, it consistently hallucinates or misses key constraints. Fixing this is rarely a simple patch. It requires a tedious, trial-and-error process of tweaking chunking strategies, retrieval methods, and system

New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget Read More »

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Copilot searched your mailbox. LiteLLM handed out admin keys. Run this 5-check audit before your stack is next

Two AI tools broke in the same way in the same two weeks, and four research teams proved it. The pattern underneath every disclosure is one sentence: enterprise AI accepts external input with no trust boundary. On June 15, Varonis disclosed SearchLeak (CVE-2026-42824), a proof-of-concept exfiltration chain in Microsoft 365 Copilot Enterprise Search. A victim

Copilot searched your mailbox. LiteLLM handed out admin keys. Run this 5-check audit before your stack is next Read More »

OpenAI Releases LifeSciBench, a 750-Task Benchmark Grading AI Models on Real Life-Science Research With Expert-Written Rubric

Most biology benchmarks ask narrow, fact-based questions with clean answers. Scientists weigh imperfect evidence and make decisions. OpenAI released LifeSciBench and it targets that gap directly. Even the strongest model passes roughly one task in three. The benchmark is far from saturated. What is LifeSciBench LifeSciBench contains 750 expert-authored tasks. They span seven workflows and

OpenAI Releases LifeSciBench, a 750-Task Benchmark Grading AI Models on Real Life-Science Research With Expert-Written Rubric Read More »