Bengaluru's New Development Minister Outlines Plan to Fix Potholes, Traffic and Flooding

Krishna Byre Gowda has taken charge as Bengaluru's new Minister for Bengaluru Development. He has outlined a multi-pronged approach to address the city's longstanding urban infrastructure challenges. His plan targets key civic issues including pothole-ridden roads, chronic traffic congestion, and recurring waterlogging. Gowda has stated his intention to improve basic civic amenities while simultaneously pursuing long-term structural solutions for the city.

"Brexit Has Done More Harm Than Good": UK PM Andy Burnham Seeks New EU Ties

Britain's membership of the EU has long been a thorny and highly divisive subject after a knife-edge majority voted to leave in a landmark referendum a decade ago.

Essex Sea Wall Breach in 2002 Doubled Thames Estuary Saltmarsh Over Two Decades

In October 2002, a deliberate sea wall breach in Essex flooded farmland and converted it into a coastal wetland. The project was backed by multiple environmental organisations with the goals of restoring natural habitats and tackling coastal erosion. The newly created wetland rapidly attracted marine life and saw a significant rise in biodiversity. Saltmarsh coverage across the Thames Estuary effectively doubled as a result of the intervention. Notably, conventional farming continued on higher ground nearby, allowing agricultural and conservation activities to coexist sustainably.

Maharaj credits Lyon as key influence a decade after their first meeting

South African spinner Keshav Maharaj has spoken warmly about his long-standing bond with Australian off-spinner Nathan Lyon, which began ten years ago. Maharaj described Lyon as a significant influence on his development as a spinner. The South African has made a habit of seeking out Lyon's knowledge and experience whenever possible. Maharaj said he tries to pick Lyon's brain 'as much as possible,' highlighting the mutual respect between the two veteran spinners.

Gurugram SUV Driver Arrested for Hitting Bike on Dwarka Expressway

A Thar SUV driver has been arrested in Gurugram, days after a hit-and-run incident on the Dwarka Expressway. The incident occurred after 8 pm on September 26, when the speeding SUV entered the service lane from the left side. The driver attempted to bypass multiple lanes at once before striking a motorcycle. Authorities tracked down and apprehended the accused in the days following the collision.

Quven

Discussion | Link

DraftKings Is Using AI to Behaviorally Target Chronic Gamblers

Article URL: https://www.eff.org/deeplinks/2026/09/draftkings-using-ai-supercharge-harms-online-behavioral-advertising Comments URL: https://news.ycombinator.com/item?id=49896050 Points: 4 # Comments: 0

MeetMind AI Agent Uses Persistent Memory to Track Relationship Context Over Time

MeetMind is an AI agent system designed to retain and build upon past meeting information rather than treating each interaction in isolation. The platform combines a frontend, API layer, AI extraction, persistent memory, and a meeting brief generator to maintain evolving relationship context. Unlike traditional input-process-output AI models, MeetMind follows a past-interaction-memory-future-action pipeline that more closely mirrors how long-term human assistants operate. Developers behind the project say building it shifted their understanding of what makes AI memory genuinely useful. Their key insight was that storing more information alone is not the goal — how that memory informs future actions is what matters.

Developer Builds AI Agent That Learns From Past Infrastructure Failures

A developer has created ASII-Hindsight, an infrastructure risk intelligence system designed to combine multiple warning signals and compare them against a memory of past failures and near-misses. The system monitors bridges, roads, and buildings using five logical agents, including a weather agent, to assess risk more holistically than single-signal approaches. Its core logic matches current conditions — such as heavy rainfall combined with poor drainage — against historical incidents to identify recurring failure patterns. The prototype uses live simulation rather than real government sensor data and is built with React, Node.js, Express, SQLite, and a local pattern-matching engine. The project emphasizes the value of near-miss data, treating close calls as critical learning opportunities for preventive action.

‘I have an ex-MLA at home’: Pension Bill lends moment of mirth to stormy J&K House

Anthropic IPO Filing Reveals 12x Revenue Growth but $518B Compute Obligations

Anthropic's IPO prospectus, reported by Reuters, shows annual revenue surged from roughly $386 million in 2024 to nearly $4.6 billion in 2025, a twelve-fold increase rare among enterprise software companies. The company posted a $42 billion GAAP net loss, though approximately $34 billion of that stems from a non-cash accounting charge tied to the rising fair value of convertible financing instruments. Compute and infrastructure spending alone reached $7.33 billion in 2025, equating to about 159 percent of gross revenue, meaning Anthropic spent more on cloud and hardware than it earned from customers before any other costs. The prospectus also discloses $518 billion in future cloud and infrastructure commitments, while nearly a quarter of 2025 revenue was concentrated in just two customers, many of whom are not bound by long-term contracts. Analysts note a structural mismatch: Anthropic is locked into multi-year fixed capacity agreements with cloud providers while its enterprise clients can reduce or cancel usage at short notice.

Blog Post Explores Potential Integration of Jev and Apache Arrow Formats

A blog post published on columnar.tech raises the question of what a crossover between Jev and Apache Arrow data formats might look like. The article explores the technical possibilities of combining these two data representation approaches. The post appeared on Hacker News, where it received 4 points and no comments at the time of indexing. The discussion touches on columnar data storage and serialization concepts relevant to data engineering. The piece appears aimed at developers and data engineers interested in interoperability between data formats.

How Persistent Memory Transforms AI Sales Agents Into Context-Aware Deal Advisors

Developers have built a deal intelligence agent that retains and applies context across a multi-week sales cycle, moving beyond stateless AI responses. In simulated testing, the agent evolved from generating generic company summaries to proactively surfacing specific deal risks and matching them with proven resolution strategies from past wins. The system relies on structured memory ingestion, partitioned per client or deal, to ensure accurate recall without cross-contamination between accounts. Key engineering lessons highlight the importance of observable recall logs to distinguish between memory retrieval failures and reasoning errors. Developers also recommend using purpose-built memory infrastructure rather than custom-built solutions to reduce maintenance overhead and keep focus on core business logic.

Immigrant Tech Worker Shares 14-Month Job Search Struggle in Shifting EU Market

A neurodivergent software professional from an outsourcing-heavy country has been unemployed for over a year after being laid off during a company reorganisation that eliminated half of his development team in Europe. Despite previously holding senior and architect-level roles in RPA and automation, he finds himself caught between being overqualified for entry-level positions and underqualified for senior ones in his new country. He attributes the difficulty to a broken hiring market, the rapid rise of AI, language barriers, and recruiters with unrealistic or dismissive expectations. Throughout the ordeal, he has worked on upskilling, portfolio building, networking, and managing his mental health through music, sport, and open conversations. He shared his experience publicly to offer solidarity and practical insights to others facing similar challenges in today's tech job market.

How trend calculation works: the math behind linear regression explained

A tutorial published on DEV Community walks through the mathematical foundations of trend calculation, a key tool in data science. The goal of trend analysis is to find a function that best fits a set of data points plotted over time, such as a company's revenue at different intervals. Because the relationship between time and measured values is stochastic, only an approximation — typically a linear function — can be fitted to the data. The quality of this fit is measured using SSE (Sum of Squared Errors) and MSE (Mean Squared Error), which quantify the difference between actual data points and the fitted line. To find the optimal parameters for the line, calculus is applied: the SSE is minimized by taking partial derivatives with respect to each parameter and setting them to zero.

Why 'Free' Icons Can Still Create Legal Headaches for Developers

Downloading a free icon does not automatically clarify what a developer is legally permitted to do with it, including whether attribution is required or redistribution is allowed. Once an SVG is optimized, renamed, and moved into a project repository, its original source and license information can easily be lost. This becomes a serious issue when a project evolves into a commercial product, open-source library, or redistributable template, changing how assets are distributed without changing the assets themselves. Developers are advised to treat icons as more than just files by preserving metadata such as origin, license type, attribution requirements, and permitted uses alongside each asset. Keeping this provenance data close to the file — rather than in browser history or chat messages — makes asset pipelines more legally sound and auditable over time.

Developer Proposes Production-Grade AI Agent Architecture for .NET Using a Control Harness

A software developer has published a technical guide on DEV Community outlining a production-ready AI agent architecture built in .NET, arguing that the AI model itself is only one component of a reliable agent system. The architecture introduces a 'harness' that governs identity, policy, execution budgets, and tool access independently of the underlying language model. A core design principle ensures the model can decide what actions to request, but a separate policy layer determines what actions are actually permitted. The approach uses interfaces and records such as AgentIdentity, AgentPolicy, and ExecutionBudget to enforce boundaries and auditability. The guide is intended as a practical implementation companion to a prior architectural argument published on LinkedIn.

Why AI customer agents must be built to escalate from day one

A new analysis argues that AI customer-facing systems should treat human escalation as a core feature rather than an afterthought, warning that agents built without it will generate confident but unsupported answers. Key triggers for handoff include empty retrieval results, explicit customer requests for a human, detected frustration, high transaction value, and legally sensitive topics such as warranty disputes. For a handoff to be effective, the system must notify the customer, assign the conversation to a named human in the CRM with full context, suspend AI replies, and log the reason for escalation. Skipping the logging step is identified as the most costly oversight, since aggregated escalation reasons form a ranked, evidence-based list of documentation gaps. The piece concludes that monthly review of escalation data is what allows teams to systematically improve an AI agent rather than guessing at its weaknesses.

Recall Tool Helps Engineers Reuse Past Incident Knowledge During New Outages

A new incident management tool called Recall, built as an Incident Memory Command Center, aims to help engineering teams draw on historical incident data when investigating new system failures. When an engineer describes a current problem, Recall retrieves relevant past incidents from a memory system called Hindsight and sends the combined context to an AI model via Groq for analysis. The tool returns a hypothesis-framed diagnosis — including suspected root cause, investigation steps, and recommended actions — while making clear that past fixes are clues, not confirmed solutions. Recall stores structured incident data in SQLite and preserves details such as failed remediation attempts and lessons learned, so future engineers avoid repeating unhelpful steps. A key feature allows side-by-side comparison of analyses run with and without historical memory, letting teams evaluate exactly what value past context added to an investigation.

How to Build a Self-Updating AI Memory Engine in n8n for Notion and Airtable

A technical guide published on DEV Community outlines how to build an autonomous RAG (Retrieval-Augmented Generation) synchronization engine using the n8n workflow automation platform. The system is designed to keep AI agents accurately informed by continuously syncing knowledge from Notion and Airtable into vector databases such as Pinecone, Qdrant, or Supabase pgvector. Unlike traditional approaches that re-embed entire datasets nightly, this engine uses delta-syncing — processing only records modified since the last run — to reduce API costs and avoid rate limit issues. It also employs token-aware hierarchical chunking to preserve document structure and idempotent vector upserts to prevent outdated or orphaned data from misleading AI agents. The architecture is built entirely within n8n using JavaScript code nodes and static workflow data, making it deployable without external caching infrastructure.