u/Single-Cherry8263

Here's a list of NO Proof class action settlements closing soon

Tinder age-pricing ($60.5M) deadline today, aug 18
California people only.
if you paid for tinder plus or gold while over 29 (since march 2015) or over 28 (since march 2016), you're already in the class, you just pick how you get paid by end of day. got a notice? no form needed.

google assistant privacy ($68M): aug 27 bought a pixel, Nest Hub, or google home since may 2016, or had assistant record you on a false "Hey Google." device buyers need proof (receipt, model, serial), privacy claims need none. payout is pro rata off a points system, not a flat number, so anyone quoting you an exact "$56" is guessing. Real range is a couple bucks up to ~$56.

youtube tv auto-renewal ($7.5M): aug 30 california residents only, billed through youtube (not the apple app store), renewed at least once between feb 2017 and oct 2021. est. ~$92, no proof. narrow class, most people bounce off the CA + billing rule.

lucent health breach: sept 5 notice-only, ~37k people. $80 flat no proof, or up to $5,500 with documented losses.

true world holdings breach: sept 8 notice-only, ~10k people, seafood distributor. $50 flat no proof, or up to $2,000 documented.

chantix ($44M, pfizer): sept 14 paid out of pocket for brand chantix between sept 2015 and sept 2021. generic varenicline does not count. self-attested, keep receipts in case they ask. payout is pro rata off what you spent.

Farmers Insurance TCPA ($1.25M): Sept 14: 2+ marketing calls or texts pushing Farmers while your number was on the do-not-call list. up to $160.

Here are the top open Data breach settlements:

1. Legends Hospitality data breach
Payout: Up to $5,000 for documented losses.
Deadline: Aug 19, 2026
Eligibility: US residents notified that their info may've been exposed in the Nov 2024, Dec 2024, or Jan 2025 Legends Hospitality data incidents
Proof needed: Receipts or statements showing out-of-pocket losses or fraud

2. Lakeview Health Systems breach
Payout: Up to $7,080, or $50 flat with no proof
Deadline: Aug 24, 2026
Eligibility: ~10,772 people notified that their personal or protected health info may've been involved in the Jan 2024 breach
Proof needed: Statements showing losses or monitoring costs

3. Lucent Health data breach
Payout: Up to $5,500 documented, or $80 flat with no proof
Deadline: Sep 5, 2026
Eligibility: ~37,000 people notified about the Oct 2023 email breach (names, SSNs, DOB, health/dental/vision plan info exposed)
Proof needed: Itemized losses plus supporting documentation

4. True World Holdings breach
Payout: Up to $2,000 documented, or $50 flat with no proof
Deadline: Sep 8, 2026
Eligibility: ~10,390 employees/customers notified after the Aug 2024 ransomware attack (names, SSNs, DOB exposed)
Proof needed: Documented loss

Check your email/mail for a notice from any of these companies before filing most claim portals require a notice ID or PIN.

here are the Claude plugins I use on daily basis.

Context7 fixes the stale-docs problem. Ask Claude about a Next.js 15 API and it'll sometimes answer from Next.js 13 muscle memory.

claude mcp add context7 -- npx -y /context7-mcp

It resolves the library first, then queries the real docs before answering. I stopped getting confidently wrong API signatures after adding this one.

GitHub MCP server connects Claude to your actual repo instead of whatever code you pasted in.

claude mcp add github --scope user --transport http https://mcp.github.com

This is the one that turns "write me a fix" into "read the issue, find the file, open the pull request." Worth the OAuth step.

Postgres MCP gives Claude read access to your schema and lets it write and run its own queries.

claude mcp add postgres -- npx -y u/modelcontextprotocol/server-postgres postgresql://readonly@localhost/mydb

Read-only connection string. Not optional. I ask questions in plain English now instead of hand-writing joins, and the schema introspection means it stops guessing column names.

Claude-Mem solves the part that bugs me most: every new session starts from zero. It writes project context, decisions, and conventions to a local memory store so I'm not re-explaining the auth pattern every Monday morning.

Brave Search MCP gives Claude live web access instead of whatever it learned during training.

claude mcp add brave-search --env BRAVE_API_KEY=YOUR_BRAVE_API_KEY -- npx -y u/brave/brave-search-mcp-server

You need a Brave Search API key first; grab one from their developer dashboard before running this. Once it's connected, Claude can pull current prices, check what actually shipped this week, or verify a claim instead of guessing from a stale training cutoff. I use it mostly for the second one, catching myself before I state something as fact.

Each one is a single claude mcp add and a restart. The plugin system that bundles skills, commands, and hooks together sits on top of this as a separate layer.

Companies usually go green when it pays, protects them, or is forced on them.

Money is the driving factor: efficient data centres are cheaper to run at scale.

Governments are already capping water use and blocking new data centres. It's cheaper to get ahead of the rules than get caught by them. The backlash against data centers is real too "clean" is a brand moat for customers and talent. Enterprise net-zero targets push companies toward greener vendors.

Will AI companies go green because it genuinely pays, or only when regulation makes dirty more expensive than clean?

AI's flex has always been about capability, bigger models, higher benchmarks, more parameters. That seems like the wrong measure now. Real intelligence is how little a model needs to do the job well, not how much it can technically do. The flex is moving from how capable to how capable per watt.

Anyone can scale up; the hard part is getting the same answer for less compute, power, and water. That's where the real research is: MoE routing, distillation, quantisation, smaller task-specific models instead of generalists. None of it shows up on a leaderboard therefore, the companies are not prioritising it.

so what should we actually be benchmarking: capability or capability per watt

I am Building an AI UI design app and need a coding model with a solid harness that's cheap and good at frontend.

I've shortlisted some:

Kimi K3 (Moonshot) — #1 on Arena.ai Frontend Code Arena right now, ahead of Claude Fable 5. 1M context, native vision. Kimi K2.5 — cheaper, modified MIT license, 256K context, strong screenshot-to-React/Tailwind. Most battle-tested for frontend so far. GLM-5.2 — best value if cost > polish, $1.10/$4.10 per million tokens on Morph. DeepSeek V4 Flash — cheapest, but text-only and verbose, eats into the savings I am open to other suggestions. suggest me best open MOE models for coding.

MoE decouples model capability from active compute. The training numbers back it up.

Dense models run every parameter on every query. MoE routes each token to a subset of specialised experts, so a trillion-parameter model fires only a fraction of its weights per token.

DeepSeek-V3: 671B total parameters, 37B active per token, ~2.6M GPU hours to train. Llama 3 405B: 30.8M GPU hours for comparable output quality. Scaling-law research on compute-matched MoE vs. dense holds this up too. MoE hits the same loss with fewer training FLOPs, and the gap widens as models scale.

Inference is a different story. Some MoE deployments draw more energy per query than similarly-sized dense models right now, because expert-layer kernels aren't as optimised as standard dense-layer ops. But it's a tooling gap, not an architecture problem.

Here are my top 10 Claude Tips after using it for 1+ Year.

• Projects: load 40 files into a Project, every answer remixes those files instead of thinking fresh. Use for repeated tasks with fixed material (client reports, weekly formats). Empty chat for anything new.
• Model switching: frame the problem in Claude Fable 5 (your first prompt matters most), switch to Claude Opus 4.8 to continue the thread. Same conversation, cheaper per message after the frame is set.
• HTML instead of images: Claude can't generate images, but it can generate HTML you export. Upload a reference image, prompt "code an HTML infographic like this one, but about [topic]." Text renders correctly every time, unlike actual image models. Export to Canva.
• Edit, don't correct: replying "no, that's wrong" leaves the bad answer in context and Claude keeps referencing it. Go back to the prompt before the bad answer, edit it, save. The mistake never existed.
• Dictation over typing: typing pre-organizes your thoughts and quietly cuts context. Talking doesn't. Use Wispr Flow, ten minutes straight on goal, constraints, what failed, no cleanup. End with "these were messy voice notes, ask clarifying questions if anything's unclear."
• Kill unused connectors: every active connector loads into every message whether you use it or not, and you pay token cost for that regardless. Turn off anything you're not actively pulling from.
• Force clarifying questions: add "ask me clarifying questions before answering if you need context." Triggers AskUserQuestion, tappable multiple-choice options instead of Claude guessing.
• Claude Code over Cowork for capability: looks like a dev tool, that's the only real barrier. Open the Code tab once, dictate something small ("build a site to track my daily runs"), see what comes back.
• Cowork's real value is parallel agents, not local file access. Give it a big task ("prepare the full client onboarding: deck, welcome email, checklist") and it splits the work across instances instead of running it sequentially.
• Cowork setup skill: run /setup-cowork with "start." It interviews you on preferences and workflow, configures itself around the answers.
• Screenshot over description: in Claude Code or any design task, a competitor's page or a napkin sketch photo gets you further than describing what you want.
• Artifacts as mini-apps: "build me an artifact that tracks [x], save data between sessions" gets you a shareable tool. Add "put a Claude coach inside with context of this artifact" and the person using it never opens Claude directly.
• Stack connectors for cross-system tasks: Slack for what was said, Granola for what got decided, Gmail for what was promised in writing. "Using Slack, Granola, and Gmail, draft three emails with three tones about [x]." Claude searches all three itself, takes a minute.
• Research mode: not a faster search, it plans, reads a stack of sources, returns a structured report. Ask a real decision question, let it run.
• Interactive charts: paste raw numbers or a CSV, ask for "an interactive chart." Renders in the chat, no formatting needed first.

we've been using small, efficient coding models for our agentic setup lately. here's the benchmark & my take on which ones we use on daily basis.

Qwen3.6-27B (dense, released April 22, 2026)

• Architecture: 27B dense, every parameter active per token
• VRAM: ~17GB at Q4_K_M, fits a 16-24GB GPU with room for context
• SWE-bench Verified: 77.2%
• Context: 262K native, up to 1M with YaRN
• License: Apache 2.0

Qwen3.6-35B-A3B (MoE, released April 15, 2026)

• Architecture: 35B MoE, 3B active parameters per token
• VRAM: ~21GB at Q4_K_M
• SWE-bench Verified: 73.4%
• Context: 262K native, up to 1M with YaRN
• License: Apache 2.0

Devstral Small 2

• Architecture: 24B dense, no MoE routing
• VRAM: ~14GB at Q4_K_M
• SWE-bench Verified: 68%
• Context: 256K native
• License: Apache 2.0

our take:

Qwen3.6-27B is the all-rounder. Runs at ~75 t/s on 24GB. Use Q6 or Q8 quant, skip NVFP4. Set presence penalty to 0.3-0.6 and top_k to 20, or it drifts. Past 40k tokens it starts going haywire, so watch your context length. Skip the Qwen Code harness, OpenCode or Pi handle it better.

Qwen3.6-35B-A3B is the speed king. It's MoE, so it still runs 25-30 t/s even on 6GB VRAM with CPU offload using --n-cpu-moe 35. Handles 200K+ context without issue. Slightly dumber than the 27B but a lot faster.

Devstral Small 2 is the specialist. Excellent on scientific or complex code, NumPy, Numba, that kind of thing, and solid tool calling. Bad at vibe-coding a game or web app from scratch. No reasoning output either, it just gives you code.

27B for general work, 35B-A3B when you need agentic workflows with a big context window, devstral for specialized tasks. Run a smart model for planning and let Qwen handle the grunt work. Skip Qwen Code.

anyone running qwen3.6-27b on a 4090 or 3090, are you seeing the same haywire behavior past 40k tokens, or is that quant-dependent?

I get they're chasing the agentic dream, and fine, maybe developers want a task runner and workspaces and tool-driven sessions. but like 1% of people are coders, and the rest of us are pasting in an email to fix the tone, or drafting a post, and my aunt doesn't need an agent workspace for that, and neither do my coworkers.

i'm on the enterprise side too and it's wrong even there, because the average office person needs a lower barrier to entry, not a nerfed codex shell with a menu of decisions in front of them. now they're pushing a "chat is dead" line they made up themselves, about the exact feature that built the whole company. chat isn't dead, they're just strangling it & making it their bad strategy.

Every update makes the simple thing harder to reach.

every watt a server pulls comes back out as heat. a single AI rack can dump 30-100+ kW into a fridge-sized space, so a data centre is really two machines, one that computes and one whose only job is moving that heat out, and water carries far more heat per liter than air.

the two water bills
on-site: water cooling the building. off-site: water evaporated at the power plant making the electricity, and per Lawrence Berkeley National Lab, that one usually runs 10-12x bigger, often 80%+ of total footprint. a site can claim near-zero water on-site while carrying a massive number upstream, unless it runs on wind or solar.

the cooling loop
heat moves from chip to a chilled water loop, then either an evaporative cooling tower (cheap on power, costs water) or dry chillers (costs power, saves water). most large sites hybrid based on outside temperature, which is why water use spikes in summer, right when supply is tightest. evaporative systems also lose water to blowdown, periodically draining mineral-concentrated water and refilling with fresh, and that discharge comes out warmer and saltier than what came in.

withdrawal vs. consumption
withdrawal is water pulled and mostly returned. consumption is water that doesn't come back, mostly evaporated, and this is what actually drains a reservoir. same facility, these numbers can differ by 10x, so "used X gallons" means nothing without knowing which one you're reading.

Tradeoff: Cool with water or cool with electricity, but not both

Multiple sources to learn this process
- Lawrence Berkeley National Lab / US DOE, 2024 Data Center Energy Report - https://www.osti.gov/servlets/purl/2572888
- IEEE Spectrum, The Real Story on AI Water Usage at Data Centers - https://spectrum.ieee.org/ai-water-usage

dont try to add everything into one CLAUDE.md. understand what these files actually are & what it can cost if done so.

AGENTS.md gets read on every single session. Every sentence in it is recurring token spend, whether the agent needs that sentence for the current task or not. That's the whole design constraint. It's now the closest thing the industry has to a shared standard, governed under the Agentic AI Foundation (the same body behind Model Context Protocol).

stop doing: writing architecture overviews. Research cited by tool vendors, architectural summaries barely move the needle on agent performance, Run exact commands; "Run the tests appropriately" gets ignored. npm run test:unit -- --coverage doesn't.

I also stopped letting an agent write its own AGENTS.md. Generated files reduced task success and increased cost in the studies I've seen, mostly by restating what the agent could already pull from the repo. A short file I edited myself is better than one a model wrote for me.

SKILL.md

Where AGENTS.md describes a project, a skill describes a capability, and it only costs tokens when it's relevant. At session start the agent reads the YAML frontmatter, just name and description. The full body loads only when a task matches the skill's domain. Reference docs and scripts inside the folder load later still. Ten skills sitting unused cost almost nothing.

That only works if the description is tight. A vague one forces the agent to open the full file just to check relevance, which defeats the mechanism. I write these narrower.

Where I draw the line between the two: a constraint every session needs goes in AGENTS.md. A capability I invoke occasionally, like a deployment sequence or a niche internal API, goes in a skill folder instead.

CLAUDE.md, .cursorrules, .windsurfrules, copilot-instructions.md, these are the tool-specific holdovers from before the industry converged on AGENTS.md. I don't hand-write any of them anymore. AGENTS.md is the source of truth, and a short sync script generates the rest. The failure mode without it: update one file, forget the other four, and you're back in the exact context drift these files were supposed to prevent.

DESIGN.md: encoding a project's visual identity as machine-readable tokens plus the reasoning behind them, so an agent generating UI code knows why a color exists and not just its hex value. Early. Narrow. Built for one slice of context instead of trying to cover everything.

AI as a technology isn't going away, and I don't expect the ai companies to change course on water and electricity use. So what, actually, is in a regular users' control here, if anything?

does it matter which model or provider you use, or is that a rounding error next to how often you use it?

What, as a consumner I can do, assuming the companies won't?

No proof of purchase needed for most of these; just check if you fall in the class before filing.

• Costco Washington residents who got a promotional email from Costco between June 2021 and July 2026 can file with no proof, up to $500 per qualifying email. Deadline Aug 24.
• Flo Period Tracker used the app in the US between Nov 2016 and Feb 2019 and logged period or pregnancy data? No proof needed; California users get double the payout. deadline Oct 15.
• RE/MAX + Keller Williams bought a home listed on an MLS in the US, closed on or before April 14, 2026. This one needs documentation (closing statement, HUD statement) to file. Deadline Aug 25.
• Equifax: this one's narrow: only if you got a specific "Duplicate Reporting Letter" from Equifax in Aug or Sept 2022. Deadline: Sep 1.
• Circle K: if you got a breach notice tied to the May 2024 Gas Express/Circle K cyberattack, you can file for a flat $50 or up to $2,000 in documented losses. Deadline: Sep 3.

my cousin runs a marketplace on WhatsApp. wants to move to a website. what is the best payment processor for a marketplace. we've got payments from multiple countries at this point & we expect to get it even bigger once we launch the marketplace.

what is the payment processor we should go with? with competitive price & not high fx rates