Computer Science and Programming

WebSocket vs SSE vs Long Polling: The Real Cost of 1,000 Events A hands-on benchmark built with a Node harness and a byte-counting TCP proxy measures the real wire cost of WebSocket, SSE, and long polling delivering 1,000 events. WebSocket used 119,692 bytes, SSE 131,596 (10% more), and HTTP/1.1 long polling 884,698 bytes (7.4x), mostly re-sent headers. On HTTP/2, long polling drops to 182,475 bytes (1.56x payload). Latency differences at low event rates were under 0.3ms across all three; long polling only degrades when events arrive faster than a round trip. Server memory for 500 idle connections favored WebSocket (6.2-6.7MB) over SSE (11MB) and long polling (10.3-10.9MB). Practical failure modes covered include idle timeouts, proxy buffering, and the six-connections-per-origin HTTP/1.1 cap on SSE. The recommendation: build SSE first for server-to-client feeds, move to WebSocket only when upstream or binary data is needed.

Dark mode toggles: two states are enough • Lea Verou (Light, Dark, System). The core argument is that users only seek out theme toggles when something is wrong — not to preemptively express intent — making the 'System' state irrelevant at the moment of interaction. Tri-state toggles expose the underlying data model rather than aligning with actual user goals, adding cognitive load and UI friction for an extremely rare use case. A well-implemented two-state toggle can still represent all three underlying states: the first click overrides to the opposite of the current resolved value, and the second click removes the override and returns to system default. Common mistakes include storing a value that matches the system preference (silently pinning the theme) or removing overrides when the OS preference changes. Exceptions where tri-state controls are appropriate include dedicated settings panels and sites that implement meaningfully different color schemes depending on the OS setting.

No Index GroupBy Optimization Dolt and Doltgres were using a secondary index for GROUP BY queries even when a full table scan would be faster. By analyzing EXPLAIN output and flame graphs, the team discovered that when a filter selects more than ~50% of rows, the secondary index lookup overhead outweighs its benefits. They added heuristics to the query coster: secondary indexes are only preferred when they select fewer than 25% of rows, while primary keys and covering indexes are always preferred. Using existing statistics histograms to estimate filter selectivity, this reduced groupby_scan latency by 57% on Dolt (144ms → 62ms) and 44% on Doltgres (147ms → 83ms), making Dolt faster than MySQL on this benchmark.

DuckDuckGo "Normal F**king Sunglasses" Sold Out In Under a Week DuckDuckGo partnered with sunglasses brand Knockaround to release "Normal F**king Sunglasses" — a satirical product mocking AI-powered smart glasses. The glasses have no camera, microphone, AI, battery, or electronics of any kind. They sold out in under a week. The collaboration is a commentary on growing surveillance concerns around AI glasses, particularly following Meta's use of Ray-Ban Meta smart glasses footage to train its AI systems and the June 2026 launch of Meta Glasses.

What's new in PHP 8.6 PHP 8.6, scheduled for release on November 19, 2026, introduces several notable features: partial function application (PFA) for prefilling closure parameters, readonly property defaults (now allowed when implementing interfaces with property hooks), a new low-level Polling API for I/O multiplexing with epoll/WSAPoll backends, a built-in clamp() function, new isReadable/isWriteable reflection methods, function parameter doc comments via ReflectionParameter::getDocComment(), a SortDirection enum, debuggable enums via __debugInfo(), and improved session security defaults (HttpOnly, SameSite=Lax). The release also includes a large set of deprecations covering legacy functions (is_double, is_integer, is_long, doubleval, strcoll, metaphone, spl_classes, spl_object_hash), ArrayIterator methods, SplFileObject CSV methods, returning from finally blocks and constructors/destructors, and reserving keywords like let, is, and namespace for future syntax.

Retiring the Copilot Billing Preview app GitHub has retired the Copilot Billing Preview app. Copilot spend can now be managed directly in GitHub billing settings, which offers a more complete view including user-level budgets, cost centers, and usage pool allocation. Users can view AI usage, set spending budgets, access user-level budget controls for organizations and enterprises, and pull raw usage data via usage reports and the billing API.

Substack writers, you need a website Writers who rely on Substack as their primary digital home are making a strategic mistake. Substack and similar platforms are distribution tools, not permanent homes — when you build on someone else's platform, you're a tenant, not an owner. The POSSE model (Publish on your Own Site, Syndicate Elsewhere) offers a better approach: publish first to a domain you own and control, then use platforms like Substack purely as distribution channels. John Scalzi's 28-year independent blog is cited as a model of digital sovereignty. Platform algorithms favor dominant narratives, terms can change overnight, and content can be lost — owning your domain protects your long-term visibility and creative independence.

The Disappearing Senior: AI and Entry Level Jobs AI is eliminating entry-level developer work, but there's a less-discussed second effect: senior engineers are quietly losing their skills by only approving AI-generated output rather than doing the work themselves. The 'reasoning muscle' atrophies when judgment is never exercised. Meanwhile, no new seniors are being produced because the junior work that used to forge them is gone. Practical countermeasures include deliberately doing one thing the hard way each week, asking AI to defend its choices rather than just accepting answers, keeping a log of AI errors, and giving juniors the 'boring' work instead of offloading it to machines. No systemic fix is coming — individual engineers must maintain their own skills deliberately.

CVE-2026-59328: Cross-Site Scripting in Eclipse Spring Boot Starter Wizard Dependency Tooltips A CVE advisory (CVE-2026-59328) has been published for a Cross-Site Scripting (XSS) vulnerability found in the Eclipse Spring Boot Starter Wizard's dependency tooltips. The advisory is hosted on spring.io and relates to the Eclipse IDE plugin used for Spring Boot project setup.

Announcing DuckDB 1.5.5 DuckDB 1.5.5 has been released as the sixth patch in the 1.5 (Variegata) line. The release includes bugfixes, performance improvements, correctness fixes, crash and internal error resolutions, and security patches. Full release notes are available on GitHub, with v2.0.0 expected in the

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How I Learned to Structure React Projects Around Change, Not Folders Technical folder structures (components, hooks, services, utils) make React repos easy to browse but hard to change, because a single feature's logic is scattered across unrelated directories. The key insight is organizing code around ownership of change rather than file type. This means colocating a feature's components, state, validation, API calls, and business rules under one feature directory. State placement should reflect lifecycle and source of truth, not convenience. Business rules belong near the feature that owns them, not in generic utils. Shared code should only be extracted when callers truly share the same meaning and change together. Finally, dependency direction matters: features should depend on stable shared primitives, not each other's internals. The real test of an architecture is how confidently a developer can locate the owner of the next change.

Locally great, globally drifting An evaluation of AI-generated frontend code in a real product reveals a recurring pattern: AI performs well in isolated, localized contexts but struggles to maintain consistency at the system level. The post argues that AI should be used as a candidate generator and validator rather than as the final decision-maker in code review.

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The Joy of Old-School Coding After a Stupid Mistake A short personal reflection on the frustration and satisfaction of debugging without AI assistance. The author made a mistake using a collection to map a one-to-one relationship in Entity Framework Core, which only integration tests caught. After resisting the urge to use Copilot, they found the fix themselves — a one-line change — and reflect on the emotional cycle of coding: frustration followed by joy.

Announcing .NET Modernization for Beginners Microsoft has released a free, open-source, hands-on course called '.NET Modernization for Beginners' to help developers upgrade legacy ASP.NET applications to .NET 10 using the GitHub Copilot modernization agent. The four-chapter course covers assessment, planning, upgrade execution, and cloud deployment to Azure App Service. Unlike typical agentic tools, the GitHub Copilot modernization agent produces transparent, editable artifacts (assessment.md, plan.md, tasks.md) that developers review and adjust before any code changes occur. A GitHub Copilot subscription and Visual Studio 2022 (17.10+) are required.

SkiaSharp for .NET SkiaSharp 4.0 is now stable, marking the biggest release in years for the cross-platform rendering library that underpins .NET MAUI, WinUI 3, and Uno Platform. Built on Google's Skia engine (which also powers Chrome and Android), SkiaSharp provides a single GPU-accelerated, pixel-perfect rendering API across Windows, macOS, Linux, iOS, Android, and WebAssembly. Version 4.0 brings the native engine current through Skia milestone m148 (28 upstream milestones), a reworked object lifecycle that eliminates use-after-free crashes, variable font support, color font palettes, animated WebP encoding, and up to 24% faster GPU rendering (30% for Uno Platform apps). Uno Platform is now a formal co-maintainer alongside Microsoft's .NET team, ensuring predictable release cadence

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DuckDuckGo browser now blocks YouTube video ads DuckDuckGo's browser now blocks most YouTube video ads, including pre-roll and mid-roll ads, across iOS, Mac, and Windows by default. Android users must enable it manually in Settings. The feature uses community-maintained filter lists from uBlock Origin, supplemented by DuckDuckGo's own compatibility rules. It works separately from the existing Duck Player feature, and both can be used simultaneously. DuckDuckGo joins Brave and Opera in offering built-in YouTube ad blocking without third-party extensions. Minor buffering delays may occur, and the feature may occasionally break as YouTube changes its ad-serving methods.

GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra. What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%. The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team. ➡️ HuggingFace