标签：#m

submitted by /u/fagnerbrack [link] [留言]

Lyft details a new pickup experience to improve reliability in gated communities, where 25–30% of rides face routing and access challenges. The system uses mapping signals, boundary detection, and routing improvements to reduce cancellations and coordination overhead between riders and drivers, highlighting how real-world constraints drive evolution in geospatial systems. By Leela Kumili

SpaceX alumni Andrew Redd is betting the ocean has vast amounts of untapped geothermal energy.

Set across mid-twentieth century Japan, RGG's latest has an entirely new fighting system.

Brutal self-assessment paints a picture of a Microsoft gaming division in crisis.

I've been teaching myself about Symbolic Regression (SR), which looks like a super exciting field. (A great intro resource below [1]). But then I was wondering: given LLMs' increasingly-growing power in generating code, which is in a way very similar to Symbolic Regression (or of course, even directly tackling symbolic regression tasks), are existing SR techniques dead? Happy to hear your thoughts. [1] ETH Zürich AISE: Symbolic Regression and Model Discovery - YouTube submitted by /u/omomom42 [link] [留言]

Lara get your gun(s).

The general shape of the problem is that every public LLM benchmark is on a saturation clock that runs from the moment of its publication to the moment a model's training corpus has eaten it. The clock has been running, on the visible benchmarks of the last five years, for somewhere between twelve and thirty months before each one is no longer useful for differentiating frontier models. The benchmarks are not failing. They are doing exactly what they were designed to do, in the order they were designed to do it, and the field has been running through them faster than the people designing them anticipated. I want to put numbers on the saturation pattern, walk through what the contamination evidence actually says, and then sit with the question of what an honest benchmark would have to look like in 2026 — because the "private held-out eval" answer that the labs are converging on has economics that are worth examining carefully before any of us salute it as the solution. The saturation timeline, with numbers HumanEval (Chen et al., OpenAI, July 2021). 164 hand-written Python problems. The benchmark was published with Codex at 28.8% pass@1; the underlying GPT-3 base model scored 0%. GPT-4 (March 2023) hit 67% in the original Technical Report. By late 2024, OpenAI's o1-preview and o1-mini both reached 96.3% pass@1 ; Claude 3.5 Sonnet sat at 93.7%. The benchmark is saturated in the operational sense — the relative spread across the top ten models is around 10 percentage points, which is too small a gap to differentiate them on, and most of the new models arrive within a percentage point or two of the ceiling. The successor variants (HumanEval+ from EvalPlus, with augmented test cases) are the field's response. Lifespan from publication to operational saturation: about 36 months. MMLU (Hendrycks et al., September 2020). 57 subjects, ~14,000 multiple-choice questions, taken from publicly-available test prep and academic sources. The problem with MMLU is not that it's satura

Cash App's AT&T-based MVNO will offer an unlimited 5G data plan for $40 per month including taxes and fees. The new mobile service is powered by Gigs, the same firm behind the Klarna mobile service that launched last year with the same pricing and is "rolling out to select users, with broader availability planned in […]

Database Migration Strategies for Next.js and Supabase Production Apps You've built your Next.js app with Supabase. It works perfectly in development. Now you need to deploy to production and realize: how do I safely change the database schema without breaking everything? Database migrations are how you version control your schema and deploy changes safely. This guide covers everything from basic migrations to zero-downtime production deployments. Prerequisites Supabase project (local and production) Supabase CLI installed Next.js application Git for version control Understanding Migrations A migration is a SQL file that changes your database schema: -- supabase/migrations/20260314120000_add_posts_table.sql CREATE TABLE posts ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4 (), title TEXT NOT NULL , content TEXT , user_id UUID REFERENCES auth . users ( id ), created_at TIMESTAMPTZ DEFAULT NOW () ); ALTER TABLE posts ENABLE ROW LEVEL SECURITY ; CREATE POLICY "Users can view own posts" ON posts FOR SELECT USING ( auth . uid () = user_id ); Migrations are: Versioned: Timestamped filenames ensure order Tracked: Supabase knows which migrations have run Repeatable: Same migrations produce same result Reversible: You can write rollback logic Setting Up Migrations Initialize Supabase locally: npx supabase init This creates: supabase/ config.toml seed.sql migrations/ Link to your remote project: npx supabase link --project-ref your-project-ref Creating Your First Migration Create a new migration: npx supabase migration new create_posts_table This creates: supabase / migrations / 20260314120000 _create_posts_table . sql Write your schema changes: -- Create posts table CREATE TABLE posts ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4 (), title TEXT NOT NULL , content TEXT NOT NULL , slug TEXT UNIQUE NOT NULL , user_id UUID REFERENCES auth . users ( id ) ON DELETE CASCADE , published BOOLEAN DEFAULT FALSE , created_at TIMESTAMPTZ DEFAULT NOW (), updated_at TIMESTAMPTZ DEFAULT NOW

When a RAID array fails, the worst thing you can do is panic and start poking at it immediately. I've seen too many cases where an impatient rebuild attempt overwrote the only good copy of data. This walkthrough covers how to safely approach a degraded or failed RAID — with ddrescue as your best friend. Step 0: Stop. Don't touch the array yet. Before running mdadm --assemble , before doing anything, clone your physical disks . A RAID 5 with one failed drive can lose everything the moment a second drive throws a read error during rebuild. This isn't hypothetical — it's how most total RAID losses happen. The golden rule: image first, recover second . Step 1: Assess the damage # Check current RAID state cat /proc/mdstat # More detail mdadm --detail /dev/md0 Look for: [UUU_] — one drive failed (underscore = missing) [UU__] — two drives failed (catastrophic for RAID 5) State: degraded , recovering , or failed Do NOT run mdadm --manage /dev/md0 --add /dev/sdX yet. Stop the array instead: mdadm --stop /dev/md0 Step 2: Clone each disk with ddrescue ddrescue is the right tool because it handles read errors gracefully: it maps bad sectors, retries them, and lets you resume interrupted sessions. Never use dd for a failing disk. Install it: # Debian/Ubuntu sudo apt install gddrescue # RHEL/CentOS sudo dnf install ddrescue Clone each RAID member to a separate image file (you need enough storage — same total size as all disks combined): # First pass: copy everything readable, skip bad sectors fast sudo ddrescue -d -r0 /dev/sda /mnt/backup/sda.img /mnt/backup/sda.log # Second pass: retry bad sectors up to 3 times sudo ddrescue -d -r3 /dev/sda /mnt/backup/sda.img /mnt/backup/sda.log Key flags: -d — direct disk access (bypass kernel cache) -r0 / -r3 — retry bad sectors 0 or 3 times The .log mapfile is critical: it lets you resume if the clone is interrupted Repeat for every disk in the array ( sdb , sdc , etc.). Step 3: Work from the images Once you have image files, assemble a soft

Announcing Limn Engine I'm excited to launch Limn Engine — a lightweight, zero-dependency HTML5 Canvas game framework for the browser. No npm install. No build step. No bloated dependency tree. Drop in a single script and start making 2D games. The Core Idea: Display + Component Limn Engine is built around two classes that cover 90% of what you need in a 2D game: Display — The Game Shell A singleton Display class that manages the canvas, runs the game loop, handles keyboard and mouse input, controls the camera, and manages scenes. Setting up a game is a one-liner: const display = new Display (); display . start ( 800 , 600 ); Let me try creating it step by step . Component — Every Object in Your Game A unified Component class that combines position, size, color/image, velocity, physics, and collision detection. No separate "Sprite" and "Body" classes — one object does it all. const player = new Component ( 40 , 40 , " blue " , 100 , 100 ); Components support three modes: Rectangle — solid color shapes for rapid prototyping Image — loaded from spritesheets or single image files Text — the Tctxt subclass for text elements with backgrounds, padding, and alignment Key Features Dual-Canvas High-Performance Rendering Call display.perform() to activate dual-canvas mode. Static backgrounds and tilemaps are drawn once to an offscreen buffer, then composited as a single drawImage() call per frame. This dramatically reduces draw calls and improves frame rates for complex scenes. display . perform (); display . start ( 800 , 600 ); Tilemap Levels Define game worlds as 2D arrays and initialize the tilemap engine with one call. Supports dynamic tile placement during gameplay — great for destructible environments and breakable blocks. display . map = [ [ 1 , 1 , 1 , 1 , 1 ], [ 1 , 0 , 0 , 0 , 1 ], [ 1 , 0 , 9 , 0 , 1 ], [ 1 , 0 , 0 , 0 , 1 ], [ 1 , 1 , 1 , 1 , 1 ] ]; display . tileMap (); Sprite & AnimatedSprite Load horizontal spritesheets and define named animation clips (idle,

Cross-border purchasing platforms commonly use marketing tactics such as coupons, registration points, order rebates, and spend-based discounts to acquire new users and boost engagement. However, public promotions are prime targets for “wool hunters” (fraudsters) who exploit batch account registration, fake orders, malicious order spamming, and combined discount abuse to drain platform benefits, causing direct financial losses. Most purchasing systems lack dedicated event risk controls, making them highly vulnerable to batch exploitation as soon as a promotion goes live. This not only leads to financial losses but also crowds out genuine user benefits and distorts campaign effectiveness. This article details Taocarts’ comprehensive anti-fraud risk control framework, which uses multi‑dimensional behavior detection, rule‑based blocking, and account risk assessment to accurately distinguish real users from malicious exploiters, ensuring fair and controllable marketing activities. First, we identify common malicious exploitation scenarios and system vulnerabilities in cross‑border platforms: Batch account registration – Using new‑user exclusive coupons and registration points to harvest benefits repeatedly. Fake order placement and cancellation – Repeatedly claiming limited‑time discounts or rebates by placing and then canceling orders. Multiple accounts from the same device/IP – Spamming orders to consume activity quotas. Illegal discount stacking – Violating platform rules by combining multiple coupons or point deductions. Activity volume manipulation – Generating fake orders to earn activity rewards or points, creating false engagement data. Traditional systems have no risk rules and cannot detect batch operations or abnormal behavior, leading to wasted marketing spend and campaigns that actually lose money. Taocarts builds a fine‑grained anti‑exploit rule engine based on four dimensions: user behavior, device information, network characteristics, and order data, ena

A lot of developers rely on scaling servers to handle performance issues, but often, the real bottleneck is just fetching the exact same data from the database over and over again. If you are dealing with read-heavy APIs and want to reduce redundant database queries, Integrate Redis caching into a Spring Boot application using Spring Data Redis. A lot of developers manually manage cache states, but Spring’s cache abstraction makes it incredibly simple to handle with just a few annotations on your service layer. If you want to see the full implementation including the application properties configuration, the Redis Cache Manager setup, and the complete REST controller code, you can check out the full write-up here: Implementing Redis Caching in Spring Boot . submitted by /u/erdsingh24 [link] [留言]

submitted by /u/ScottContini [link] [留言]

I played the ultimate game of strategy on a variety of smart chess boards to find the best for online and in-person matches.

Summer is almost here, and that means getting outside, having fun in the sun, and touching grass. But if you need to cool down for a bit, allow me to sing the praises of the library. If your local library system is anything like mine, it probably offers a lot of great things for you […]

Bluesky is launching a communities feature this year, according to its head of product.

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online. According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other…

AI aside, Golden Gate includes a bunch of subtle-but-helpful improvements.