Posts

Taalas is replacing programmable GPUs with hardwired AI chips to achieve 17,000 tokens per second for ubiquitous inference

By
Image
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 back. According to Taalas team, if we want AI to be as common and cheap as plastic, we have to stop ‘simulating’ intelligence on general-purpose computers and start ‘casting’ it directly into silicon. The Problem: The ‘Memory Wall’ and the GPU Tax The current cost of running a Large Language Model (LLM) is driven by a physical bottleneck: the Memory Wall . Traditional processors (GPUs) are ‘Instruction Set Architecture’ (ISA) based. They separate compute and memory. When you run an inference pass on a model like Llama-3, the chip spends the vast majority of its time and energy shuttling weights from High Bandwidth Memory (H...
Post a Comment
Read more

VectifyAI Launches Mafin 2.5 and PageIndex: Achieving 98.7% Financial RAG Accuracy with a New Open-Source Vectorless Tree Indexing.

By
Image
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 is attempting to close this gap with the launch of Mafin 2.5 , a multimodal financial agent, and PageIndex , an open-source framework that shifts the industry toward ‘Vectorless RAG.’ The Problem: Why Vector RAG Fails Finance Traditional RAG relies on semantic similarity. If you ask about ‘Net Income,’ a vector database looks for chunks of text that sound like net income. However, financial documents are layout-dependent. A number in a cell is meaningless without its header, and those headers are often stripped away during traditional PDF-to-text conversion. This is the ‘garbage in, garbage out’ trap: even the smarte...
Post a Comment
Read more

A Coding Guide to Instrumenting, Tracing, and Evaluating LLM Applications Using TruLens and OpenAI Models

By
In this tutorial, we focus on building a transparent and measurable evaluation pipeline for large language model applications using TruLens . Rather than treating LLMs as black boxes, we instrument each stage of an application so that inputs, intermediate steps, and outputs are captured as structured traces. We then attach feedback functions that quantitatively evaluate model behavior along dimensions such as relevance, grounding, and contextual alignment. By running multiple application variants under the same evaluation setup, we show how TruLens enables disciplined experimentation, reproducibility, and data-driven improvement of LLM systems. Copy Code Copied Use a different Browser !pip -q install trulens trulens-providers-openai chromadb openai import os, re, getpass from dataclasses import dataclass from typing import List, Dict, Any import numpy as np import chromadb from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction from openai import OpenAI ...
Post a Comment
Read more

Forget Keyword Imitation: ByteDance AI Maps Molecular Bonds in AI Reasoning to Stabilize Long Chain-of-Thought Performance and Reinforcement Learning (RL) Training

By
Image
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 have been looking at reasoning the wrong way. Instead of just words or nodes, effective AI reasoning has a stable, molecular-like structure . https://ift.tt/LfaHBTv The 3 ‘Chemical Bonds’ of Thought The researchers posit that high-quality reasoning trajectories are held together by 3 interaction types. These mirror the forces found in organic chemistry: Deep Reasoning as Covalent Bonds: This forms the primary ‘bone’ of the thought process. It encodes strong logical dependencies where Step A must justify Step B. Breaking this bond destabilizes the entire answer. Self-Reflection as Hydrogen Bonds: This acts as a stabiliz...
Post a Comment
Read more

A New Google AI Research Proposes Deep-Thinking Ratio to Improve LLM Accuracy While Cutting Total Inference Costs by Half

By
Image
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 team reveals that simply adding more tokens to a response can actually make an AI less accurate. Instead of counting words, the Google researchers introduce a new measurement: the Deep-Thinking Ratio (DTR) . https://ift.tt/ymIMfiU The Failure of ‘Token Maxing ‘ Engineers often use token count as a proxy for the effort an AI puts into a task. However, the researchers found that raw token count has an average correlation of r= -0.59 with accuracy. This negative number means that as the model generates more text, it is more likely to be wrong. This happens because of ‘overthinking,’ where the model gets stuck in loops, repeats redundant steps, or amplifies its own mistak...
Post a Comment
Read more

How to Design an Agentic Workflow for Tool-Driven Route Optimization with Deterministic Computation and Structured Outputs

By
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. We integrate geographic calculations, configurable speed profiles, traffic buffers, and multi-stop route optimization, ensuring the agent behaves deterministically while still reasoning flexibly through tools. Copy Code Copied Use a different Browser !pip -q install -U langchain langchain-openai pydantic import os from getpass import getpass if not os.environ.get("OPENAI_API_KEY"): os.environ["OPENAI_API_KEY"] = getpass("Enter OPENAI_API_KEY (input hidden): ") from typing import Dict, List, Optional, Tuple, Any from math import radians, sin, cos, sqrt, atan2 from pydantic import Ba...
Post a Comment
Read more

Is There a Community Edition of Palantir? Meet OpenPlanter: An Open Source Recursive AI Agent for Your Micro Surveillance Use Cases

By
The balance of power in the digital age is shifting. While governments and large corporations have long used data to track individuals, a new open-source project called OpenPlanter is giving that power back to the public. Created by a developer ‘ Shin Megami Boson ‘, OpenPlanter is a recursive-language-model investigation agent. Its goal is simple: help you keep tabs on your government, since they are almost certainly keeping tabs on you. Solving the ‘Heterogeneous Data’ Problem Investigative work is difficult because data is messy. Public records are often spread across 100 different formats. You might have a CSV of campaign finance records, a JSON file of government contracts, and a PDF of lobbying disclosures. OpenPlanter ingests these disparate structured and unstructured data sources effortlessly. It uses Large Language Models (LLMs) to perform entity resolution . This is the process of identifying when different records refer to the same person or company. Once it connec...
Post a Comment
Read more