Delhi Assembly gets hoax bomb threat email

How to Enable HTTP/2 and HTTP/3 in C# for Faster Network Performance

Developers using C# can significantly improve application network performance by upgrading from HTTP/1.1 to HTTP/2 or HTTP/3 via .NET's HttpClient. HTTP/2, supported since .NET Core 3.0, introduces multiplexing and HPACK header compression, reducing header size by up to 95% and allowing multiple requests over a single TCP connection. HTTP/3, available in stable form since .NET 7, builds on the QUIC protocol over UDP, eliminating head-of-line blocking and enabling faster connection establishment through combined transport and security handshakes. Developers can configure the desired protocol version using HttpVersionPolicy settings on HttpClient or individual request messages. A fallback strategy can also be implemented to attempt HTTP/3 first, then HTTP/2, before defaulting to HTTP/1.1.

From Borrowed Rifles to Asian Games: Kerala Shooter Vidarsa Vinod's Inspiring Rise

Vidarsa K Vinod is set to represent India at the Asian Games after beginning her shooting journey through a casual NCC trial. Despite financial hardships and a lack of proper equipment, she continued to pursue the sport with determination. A turning point came when her husband motivated the family to buy her first rifle, enabling more structured training. She also trained in Delhi using a borrowed rifle, gradually building her skills and competitive record. Vinod now looks forward to gaining valuable experience on the Asian Games stage.

How Four Conflicting Timestamp Formats Silently Corrupted Months of Data

A data reconciliation system had been producing small daily discrepancies for months, with errors manually corrected each time without deeper investigation. The root cause was discovered only after a daylight saving clock change caused a larger spike: four separate systems were all writing to identically named timestamp columns but using different time zone conventions. The web app used UTC, a legacy batch loader used server local time that silently shifted meaning after a data centre migration, a third-party feed's time zone offset was being discarded by the parser, and an internal tool stored dates with no time, defaulting to midnight in whatever zone the database connection used. Errors went undetected because most activity fell mid-day, where an hour's ambiguity rarely crosses a date boundary — failures clustered around late-evening transactions, daylight saving weekends, and cross-system duration calculations. The team resolved the issue by standardising all new storage to UTC, separately recording local time where relevant, and flagging the ambiguous historical data range as lower confidence rather than attempting an unreliable backfill.

Developer Builds AI Posting Assistant, Discovers It Fabricated a Promise From Test Data

A developer building an AI-powered content assistant for LinkedIn found the agent misread internal Zapier validation posts as public content, incorrectly inferring an unfulfilled promise to followers. The tool was designed to analyze recent post history and suggest three topic angles rather than generating a draft immediately. During early testing, the agent treated its own interpretations of historical data as confirmed facts, a flaw the developer addressed by adding a rule requiring the agent to flag inferences and seek confirmation. A secondary gap emerged when the agent suggested covering a topic the developer had already written about elsewhere that same morning, since the skill had no visibility into external platforms. The experience highlighted two practical pitfalls in agent design: polluted training data can produce confident but false conclusions, and an agent's awareness is strictly limited to the data sources explicitly provided to it.

AWS Bedrock AgentCore Simplifies Deploying AI Agents to Production at Scale

AWS Bedrock AgentCore is a managed service that allows developers to deploy local AI agents built with frameworks like Strands, LangGraph, or CrewAI directly to production without custom backend infrastructure. In the second part of a tutorial series, a dependency-auditing agent called Release Radar — previously running only on a local machine — is deployed to AWS using AgentCore via a simple CLI workflow. The service automatically handles hosting, scaling, IAM permissions, and observability, eliminating the need to manually configure Lambda functions or API Gateway. Deployment speed depends on package size: bundles under 250MB are pushed to S3 and deploy in seconds, while larger packages with native compiled extensions follow a slower container-build path. The tutorial also highlights input validation as a critical security step, since the agent reads attacker-writable content like repository descriptions and release notes that could contain prompt-injection attempts.

Dev Guide Proposes Seed-Pinned Merge Gates to Catch Flaws in Agent-Written Tests

A software engineering post on DEV Community argues that AI agent-generated patches should not be merged solely on the basis of new example tests, which it says only reflect what the agent already knows. The author proposes a three-file merge gate system — using a contracts file, a seed ledger, and a flake-freeze registry — to enforce property-based testing at public API boundaries under reproducible random seeds. Under this policy, a patch that adds only example assertions is blocked, while one that fails a contract under a recorded seed is rejected with the seed and shrunk input attached as evidence. Flaky failures must be frozen with a pinned seed rather than skipped, and the freeze ledger is strictly human-owned to prevent agents from editing it. The system is designed to reverse the incentive for agents to write self-validating tests by requiring every legal input to satisfy the pre-existing contract, not just the fixtures the patch can see.

Six Go SaaS boilerplates compared: features, licences, and trade-offs

A 2025 roundup evaluates six Go-based SaaS boilerplates — goilerplate, Pagoda, LastSaaS, SaaS Boost Kit, Go SaaS Startup Kit, go-saas/kit, and GoVueKit — against criteria including multi-tenancy, billing integration, transactional email, and an admin interface. The guide draws a clear distinction between a true SaaS boilerplate and a basic web starter, arguing that only kits offering organisations, role-based access control, and webhook-driven billing qualify. Frontend approach emerges as the primary decision factor, with some kits favouring server-rendered HTMX while others ship full JavaScript SPAs using React or Vue 3. Licence terms also vary significantly, ranging from permissive MIT to commercial one-time purchase models. The article was authored by the team behind GoVueKit and originally published on the GoVueKit website, representing a partial conflict of interest readers should note.

OpenAI builds AI research intern, targets fully autonomous scientist by 2028

OpenAI has successfully developed an automated research intern, fulfilling a goal CEO Sam Altman had set for 2025. The company now aims to create a fully independent AI scientist by 2028, marking a significant leap in its ambitions. Autonomous AI tools are already handling laboratory operations and corporate troubleshooting tasks. The rapid pace of AI advancement has split the technology industry between supporters and those who remain skeptical. Meanwhile, rising compute costs are prompting many companies to reconsider how broadly they deploy AI systems.

Why You Should Always Verify the Sources Behind AI Search Answers

AI search tools can produce fluent, convincing answers that are factually misleading when their cited sources do not actually support the claims made. A practical evaluation method involves testing each citation across four criteria: whether the link opens, whether the source is primary, whether the passage truly supports the specific claim, and whether the date and geographic scope match. Claims should be scored as supported, partially supported, unsupported, or uncheckable to clearly separate reliable information from questionable content. Different AI search platforms serve distinct research needs — for example, Perplexity suits public web synthesis, Glean handles private enterprise knowledge, and tools like AMiner and Elicit are better suited for academic discovery. The core advice is that a well-cited short answer is more valuable than a polished response built on unverifiable claims.

'Character assassination must stop': Kangana slams targeting of TN actress

Stale Code Comments Can Mislead AI Agents Into Reversing Intentional Changes

A developer analysis published on DEV Community warns that outdated comments in code files pose a unique risk when AI coding agents are involved. Unlike human readers who often treat comments as passive documentation, AI agents incorporate every comment into their active context when processing a file. This means a comment that no longer reflects the current code can cause a subsequent AI agent to treat the outdated description as the source of truth and revert deliberate changes. The problem compounds across multiple sessions, where stale comments can propagate into tests and documentation, creating false corroboration that gives later agents high confidence to undo correct code. The author demonstrated the issue through a structured experiment using local and API-based language models, showing that contradictory context — not model incompetence — drives the regressions.

Why Silence in Work Chats Is Not Agreement — and How to Fix It

A widely shared piece on DEV Community by Asael Shinder warns professionals against interpreting unanswered messages as approval for decisions. The author argues that silence is inherently ambiguous — it can signal anything from oversight to unspoken disagreement — yet teams routinely treat it as a green light. To avoid costly misalignments, Shinder recommends setting explicit deadlines for responses, naming specific stakeholders, and asking direct questions that cannot simply be ignored. He also advises opting for brief voice calls on high-stakes matters, since verbal reactions often reveal hesitation that text threads miss. Equally, the piece reminds readers that staying quiet themselves will be interpreted as consent, making it important to register even tentative objections in writing promptly.

How a Default Dropdown Choice Became a Four-Year Architecture Regret

A proof-of-concept deployment made in an afternoon locked a company's production infrastructure into a cloud region that was never deliberately chosen — simply because it was the default. Over 18 months, the decision hardened around a production database, object storage, and years of regulated data, with no one ever having made a conscious choice. The consequences compounded quietly: latency for most users, legal uncertainty over data residency, reliance on outdated self-managed services, and ongoing cross-region data transfer costs. Author Serguey Shinder argues that cloud region selection is one of the few genuinely irreversible choices in an otherwise elastic environment, making it far stickier than it appears at the prototype stage. He now advocates treating a handful of foundational choices — including region, identity provider, and primary datastore — as one-way doors that warrant at least a brief, documented rationale even during early experimentation.

Haldwani turns into fortress ahead of Supreme Court verdict on railway land dispute

Xiaomi 18 Fold debuts as a wider, more powerful rival to Samsung's Galaxy Z Fold 8

Xiaomi has unveiled the 18 Fold, a short, wide book-style foldable smartphone, entering a market segment also being targeted by Apple in the same week. The device was previewed at IFA, where it drew comparisons to Samsung's Galaxy Z Fold 8 but stood out with superior specifications. The 18 Fold features a roughly 1.4:1 aspect ratio across its 7.58-inch inner and 5.38-inch outer displays, making it both shorter and wider than Samsung's offering. It marks Xiaomi's first book-style foldable since the Mix Fold 4 in 2024, following a design trend also adopted by Huawei and Samsung.

Bit2Me launches dedicated unit to assist law enforcement in tracing crypto assets

Spanish crypto exchange Bit2Me has established a specialized unit focused on supporting law enforcement agencies in tracking and recovering cryptocurrency assets. The move formalizes investigative work the company had already been conducting on an informal basis. In 2025, Bit2Me helped process approximately 1.5 million euros worth of seized cryptocurrency on behalf of agencies. Notable clients of these past operations include major international bodies such as Interpol and Europol. The dedicated unit signals a growing role for crypto exchanges in aiding global financial crime investigations.

Build Codeless AI Eval Dashboards Using Google Data Studio

A tutorial series on designing and visualizing AI evaluations concludes with a guide to building interactive dashboards in Google Data Studio without writing code. Users connect a CSV export from earlier evaluation runs to Data Studio via Google Sheets to create fully functional, stakeholder-friendly dashboards. The setup includes a pre-configured Master Dashboard Template with pre-styled components such as bubble charts and a Pivot Table Heatmap. Key interactive filters allow users to isolate model accuracy from infrastructure failures and separate baseline cohorts from active skill interventions. The dashboard is aimed at non-technical stakeholders — including product managers and executives — who need to explore AI performance data without developer tools.

New ‘eye in the sky’: Isro’s EOS-05 satellite settles into geosynchronous orbit

Vine Copulas: The Math Tool That Exposes Why Diversified Portfolios Fail in Crashes

Standard correlation models used in finance assume asset relationships are linear, symmetric, and stable — but all three assumptions break down during market crises. In normal conditions, correlations between major asset classes range from 0.3 to 0.6, yet during crashes they converge toward 1.0, meaning gold, bonds, and stocks all fall together. The root cause is that Gaussian copulas, the traditional default, mathematically cannot model joint tail risk — the tendency of assets to crash simultaneously under extreme stress. Vine copulas, a technique borrowed from insurance catastrophe modeling, address this by decomposing multivariate dependency structures into pairs, allowing analysts to assign different tail-behavior models to different asset relationships. This approach lets risk modelers capture the real-world phenomenon where a portfolio that appears diversified in calm markets effectively becomes a single correlated bet during a crisis.