Bengal BJP minister backs non-veg during Durga Puja

ED wants Kerala police to file FIR against Pinarayi Vijayan, daughter Veena

How updating your AWS CLI config can eliminate repeated SSO login prompts

Developers using AWS IAM Identity Center frequently encounter token expiry errors that force them to run 'aws sso login' multiple times a day. The root cause is often an outdated AWS CLI config file that uses a legacy format, which does not support refresh tokens properly. AWS SSO actually relies on two tokens — a short-lived access token (~8 hours) and a longer-lived refresh token (up to 90 days) — where the latter is meant to silently renew the former. The legacy config style repeats SSO settings inside every profile and never shares a refreshable session across them. Migrating to the modern 'sso-session' block in ~/.aws/config centralises session settings, enables refresh token support, and allows a single login command to cover all configured profiles.

Beyond Detection: How Edge AI People Counters Actually Work End to End

Building a functional edge AI people counter requires five distinct stages beyond simple person detection: frame capture, image preprocessing, detection, multi-frame tracking, and conversion of movement into entry or exit events. Each stage introduces potential errors, from uneven frame delivery and resizing artifacts to trackers assigning new IDs mid-doorway, all of which compound into inaccurate final counts. A tracker assigns temporary IDs to keep detections linked to the same individual across frames, enabling a line-crossing algorithm to register a visit only when a tracked path genuinely transitions from one side to the other. Occupancy figures are derived as running state — prior occupancy plus entries minus exits — meaning a single missed event corrupts the count until manually corrected. The article emphasizes that most demos stop at detection and frame-rate display, obscuring the substantial engineering needed before a system can reliably tell a retailer how many visitors entered a store.

PM Modi Launches Rs 35,000 Crore Projects in Vadodara, Targets Opposition

Prime Minister Narendra Modi visited Vadodara, Gujarat on Tuesday to inaugurate development projects valued at over Rs 35,000 crore. He stated that these initiatives would accelerate India's progress toward becoming a developed nation, referred to as Viksit Bharat. During the event, Modi also directed remarks at the opposition, asserting that truth would ultimately prevail over falsehood. The visit underscored the government's focus on large-scale infrastructure and development investment in Gujarat.

From Pune Orphanage to Four World Cup Wins: Lisa Sthalekar's Cricket Story

Lisa Sthalekar was born in Pune, India, and spent her early years in an orphanage before being adopted by an Australian family. She went on to build a distinguished career in women's cricket, scoring over 1,000 ODI runs and claiming 100 wickets. As captain of the Australian women's team, she was a key figure in four World Cup championship victories. She brought her career to a memorable close with a crucial catch in the 2013 World Cup final.

Developer Proposes Sudoku-Inspired Task Matrix to Fix Kanban Capacity Blind Spots

A developer has designed a workflow management system that applies Sudoku puzzle logic to team task boards, addressing what they describe as a core flaw in tools like Jira and Azure DevOps. The system uses intersecting row and column capacity limits, where the stricter of the two constraints governs how many tasks can occupy any given cell. This prevents individual contributors from being silently overloaded while broader status columns appear unoccupied. The framework is built around eight defined workflow columns, from a general backlog through to a hard-capped work-in-progress limit of five active tasks, and is intended to run on a physical whiteboard or self-hosted markdown setup. Its stated goal is to give workers structural control over new task assignments rather than leaving capacity decisions solely to managerial discretion.

GPT-5.6 Sol jumps from 13.3% to 38.3% on ARC-AGI-3 with config changes alone

OpenAI tested GPT-5.6 Sol on the ARC-AGI-3 benchmark under two different API configurations without changing the model itself, and the score jumped from 13.3% to 38.3% in Relative Human Action Efficiency while using one-sixth the output tokens. The key differences were retaining the model's reasoning across turns and using history compaction instead of truncating older context once conversations exceeded 175,000 characters. The default harness discarded private reasoning after every move and dropped earlier actions as context filled, effectively forcing the model to re-derive its understanding of game rules from scratch each turn. With the adjusted configuration, GPT-5.6 Sol solved all six levels of a game where no frontier model had previously passed the first level on the public leaderboard. The findings suggest that a significant portion of the performance gap between frontier models and human testers may stem from agent loop design rather than model capability.

Six-Dimension Framework for Evaluating AI Agent Memory Engines in 2026

As AI agent development matures, selecting the right memory engine has become a critical architectural decision rather than an afterthought, according to a technical analysis published in August 2026. The evaluation framework proposed covers six key dimensions: memory model structure, retrieval quality benchmarks, LLM dependency risk, production performance, deployment and data sovereignty, and long-term vendor risk. Five broad categories of memory solutions currently exist, ranging from DIY RAG assemblies and managed cloud services to framework-bound memory, tiered-compression systems, and heavy academic graph-database stacks, each with distinct trade-offs. Managed cloud services raise data sovereignty concerns for regulated industries, while framework-bound memory creates lock-in risks, and tiered-compression approaches can lose retrieval accuracy through lossy abstraction. The analysis was authored by the team behind NylonME, a self-hosted, graph-network-based memory engine, who disclosed their conflict of interest and included their product's weaknesses alongside competitor comparisons.

Open-Source Memory Engine NylonME Doubles LoCoMo Recall Score from 47% to 84%

The NylonME memory engine improved its LoCoMo evidence-recall score from 47.1% to 84.6% over two weeks of iterative development, as documented by its developers. The initial baseline relied solely on lexical matching, which failed to retrieve facts phrased differently from the query. Key improvements included integrating bge-m3 vector embeddings for semantic retrieval, a dual-layer write architecture that preserves both raw conversation text and LLM-distilled facts, and a final vector reranking step. An ablation test revealed that using only LLM-distilled abstract facts actually dropped the score to 67.3%, underscoring the importance of retaining verbatim conversation data. The developers published the full record of both successful changes and failed experiments, citing a lack of transparent benchmarking in the memory-systems field.

x402 Protocol Brings HTTP-Native Micropayments to Autonomous AI Agents

The x402 protocol proposes a standardized way for AI agents to make machine-to-machine payments using HTTP status code 402, which has been reserved but largely unused for decades. Traditional API billing models — relying on subscriptions, credit cards, or long-lived API keys — create significant inefficiencies for autonomous agents that must access dozens of services independently. x402 replaces pre-established billing relationships with an on-demand request-response payment handshake, enabled by low-latency Layer 2 blockchains and stablecoins. When an agent requests a paid endpoint, the server responds with payment details, the agent submits a on-chain transaction, and the server verifies the proof before granting access. The protocol includes safeguards such as block-age checks and transaction log parsing to prevent replay attacks and double-spending.

Another French museum heist: 4 Renoir paintings stolen, one dropped while fleeing

Another French museum heist: 4 Renoir paintings stolen, one dropped while fleeing

MCP server optimisation cuts AI site build time from 30 minutes to 4

A development team building websites via AI prompts discovered their 30-minute landing page build time was dominated not by component generation but by image uploads, which alone consumed 10 of 14 minutes after an initial round of optimisations. The root cause was that AI agents encode file uploads as base64 text, forcing the language model to generate thousands of tokens character-by-character for even small images. A secondary issue was that every write operation returned the full saved record, causing roughly 55 KB of redundant JSON to echo back per page build and inflating token usage significantly. The team addressed both problems at the server level by enabling inline image ingestion via URL — so bytes never pass through the model — and adding a verbose flag to suppress unnecessary response payloads. The combined changes reduced a full site build from 30 minutes to 4 minutes and cut token consumption from approximately 225,000 to 40,000.

Better AI Coding Results: How Structured Prompts Improve Cursor Output

Developers using Cursor AI often receive poorly fitting code because vague prompts like 'build this feature' give the tool insufficient context about a project's architecture, conventions, or patterns. A structured prompting approach recommends supplying context, constraints, scope, and code examples rather than broad one-line requests. Setting up a project-level configuration file such as '.cursorrules' can lock in rules around folder structure, state management, and naming conventions so every prompt is interpreted consistently. Breaking large feature requests into smaller, specific tasks — such as creating a single repository class — reduces incorrect assumptions in the generated output. The guide includes reusable prompt templates and real Flutter and Laravel examples to help developers treat AI assistants as structured collaborators rather than open-ended guessing tools.

React Native Release Checklist: Key Steps to Avoid a Failed App Launch

A seasoned React Native developer has published a practical pre-launch checklist drawn from years of shipping apps to both the Apple App Store and Google Play Store. The guide emphasizes early setup of developer accounts, permanent bundle IDs, and compliant privacy policies, warning that mismatches between data disclosures and actual app behavior are a common rejection trigger. It recommends automating builds and signing using tools like Fastlane or Expo EAS to eliminate manual errors on release day. Physical device testing is stressed as essential, particularly on budget Android handsets from manufacturers like Samsung and Xiaomi, where custom OS skins often introduce unexpected bugs. The checklist aims to help developers avoid the operational failures — rejected builds, signing mishaps, and untested edge cases — that derail otherwise ready products at launch.

Adani seeks to get a hold of ₹37,500 cr coal scheme

How to get Rs 3L/month from Rs 2 crore EPF fund

Unusual sighting in an Odisha forest: 5 baby orangutans

Congress Bets on Sachin Pilot to Deliver Punjab After Years on Sidelines

The Indian National Congress has turned to veteran leader Sachin Pilot, giving him a significant role after a prolonged period of political waiting within the party. Pilot, who had long been sidelined despite his prominence, is now being positioned as a key figure for the party's prospects in Punjab. The move signals a strategic shift by Congress to leverage Pilot's political capital and public profile. The party hopes that Pilot's patience and persistence will translate into electoral gains in the crucial northern state.