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Taalas is replacing programmable GPUs with hardwired AI chips to achieve 17,000 tokens per second for ubiquitous inference
In the high-stakes world of AI infrastructure, the industry has operated under a singular assumption: flexibility is king. We build general-purpose GPUs because AI models change every week, and we need programmable silicon that can adapt to the next research breakthrough. But Taalas, the Toronto-based startup thinks that flexibility is exactly what’s holding AI…

VectifyAI Launches Mafin 2.5 and PageIndex: Achieving 98.7% Financial RAG Accuracy with a New Open-Source Vectorless Tree Indexing.
Building a Retrieval-Augmented Generation (RAG) pipeline is easy; building one that doesn’t hallucinate during a 10-K audit is nearly impossible. For devs in the financial sector, the ‘standard’ vector-based RAG approach—chunking text and hoping for the best—often results in a ‘text soup’ that loses the vital structural context of tables and balance sheets. VectifyAI…

A Coding Guide to Instrumenting, Tracing, and Evaluating LLM Applications Using TruLens and OpenAI Models
def normalize_ws(s: str) -> str: return re.sub(r”\s+”, ” “, s).strip() RAW_DOCS = [ { “doc_id”: “trulens_core”, “title”: “TruLens core idea”, “text”: “TruLens is used to track and evaluate LLM applications. It can log app runs, compute feedback scores, and provide a dashboard to compare versions and investigate traces and results.” }, { “doc_id”: “trulens_feedback”,…

Forget Keyword Imitation: ByteDance AI Maps Molecular Bonds in AI Reasoning to Stabilize Long Chain-of-Thought Performance and Reinforcement Learning (RL) Training
ByteDance Seed recently dropped a research that might change how we build reasoning AI. For years, devs and AI researchers have struggled to ‘cold-start’ Large Language Models (LLMs) into Long Chain-of-Thought (Long CoT) models. Most models lose their way or fail to transfer patterns during multi-step reasoning. The ByteDance team discovered the problem: we…

A New Google AI Research Proposes Deep-Thinking Ratio to Improve LLM Accuracy While Cutting Total Inference Costs by Half
For the last few years, the AI world has followed a simple rule: if you want a Large Language Model (LLM) to solve a harder problem, make its Chain-of-Thought (CoT) longer. But new research from the University of Virginia and Google proves that ‘thinking long’ is not the same as ‘thinking hard’. The research…

How to Design an Agentic Workflow for Tool-Driven Route Optimization with Deterministic Computation and Structured Outputs
In this tutorial, we build a production-style Route Optimizer Agent for a logistics dispatch center using the latest LangChain agent APIs. We design a tool-driven workflow in which the agent reliably computes distances, ETAs, and optimal routes rather than guessing, and we enforce structured outputs to make the results directly usable in downstream systems.…
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