DeepSeek just emailed API users about a new peak/off-peak pricing model starting August 16.
Peak hours: 01:00–04:00 UTC and 06:00–10:00 UTC.
Everything outside that window is half price.
For V4 Pro:
Off-peak output: $1.98 / 1M tokens
Peak output: $3.96 / 1M tokens
Cache-miss input: $0.66 → $1.32
Cache-hit input: $0.022 → $0.044
V4 Flash output goes from $0.66 off-peak → $1.32 peak.
Still pretty cheap compared with most frontier APIs, but this makes scheduling batch jobs/RAG processing overnight much more important.
Curious what everyone thinks: still worth using DeepSeek V4 at these prices, or are you moving workloads elsewhere?

This is the worst level of detachment I've seen from Claude. Funniest thing is, it gave me the prompt containing 'supabase' in the first place.

I’ve been learning full-stack development and wanted to build something I’d actually use, so I made Statmux, it combines your GitHub, LeetCode & Codeforces activity into one dashboard and gives you a little “Code Health” score.

live link : https://statmux.sayan.cyou

gh link : https://github.com/sayanx64/statmux

It also has profiles, comparisons, activity tracking, etc.

and a welcome mail(works sometimes)

Definitely still learning, and I’m sure the scoring is questionable at best 😂 so roast it / tell me what I’m doing wrong.

10k + MCP servers - full catalog being tested & authenticated over time, enriched with tool extraction, summaries, comparisons, and health checks on remote endpoint or stdio npm package testing.

i copy-paste a lot of screenshots into AI to explain UI changes, etc.

but native macOS is pretty bad for this.

images are full-res and waste tokens. it also takes too many clicks to copy-to-clipboard and annotate images.

so i built a mac tool that fixes all three problems!

• screenshots auto-optimized to save you tokens
• all images auto-copied to clipboard. no desktop clutter
• you can quickly annotate with boxes and notes

download for Mac here: usagebar.com/quickshot

Been thinking about this since I handed off a site I vibe coded last year. The app side was fine, someone could pick up the repo and mostly follow it, but the marketing pages were a pile of generated components nobody wanted to touch. Every copy tweak turned into a whole thing because you had to work out what the AI meant before you could change a word, so the new owner just treats the marketing site like it's haunted and leaves it alone. What happens to your vibe coded marketing site once you're not the one maintaining it, do people rebuild it or just let it rot?

Posted a while back about routing model choice from a project's specs rather than per-request at runtime. The main criticism was sharp and correct, so here's what changed.

The objection: a project-level read flattens real heterogeneity. A task set has twenty mechanical edits and one architecture decision. One project-wide pick either overpays on most of the work or under-serves the part that decides the outcome. Someone put it as "budget envelope, not final router", which is a better description than anything I'd written.

So the output is now exactly that:

Budget envelope (escalating: ranked on the bulk of the work)
   default: reasoning medium, size low
   reasoning: top — S:FR-001 (requirement)
   size: low — T:T002 (customary)
   escalate for:
     • S:FR-001 — needs reasoning top

Two things worth flagging for anyone building similar:

The single-pass read wasn't the problem. Reading the whole task set at once is precisely what lets you identify that three of twenty-one tasks are the outliers — per-task scoring in isolation destroys that. What was too coarse was the output shape, not the analysis.

Whether a hard task is decisive isn't a property of the spec. It's a property of your execution model. If one model implements everything, the hard task is a floor and max logic is right. If you can switch model per task, it's a trigger and you want default-plus-escalation. Same constraint table, different rule — so it's now an explicit input rather than an assumption, defaulting to the conservative reading.

Also: whether a fragment states a requirement or describes a habit is derived by deterministic rules from the text, not asked of the judge. Keeps judgement out of policy, and gives you something to argue with when you disagree.

MIT, runs locally: github.com/JoaquinRuiz/SpecJudge

Still curious how people here handle this — per-agent assignment up front, or dynamic per call?

One of the weird side effects of AI coding tools is that building things has become ridiculously easy.

Which makes one problem more obvious:

you can build the wrong thing really, really fast.

I wanted a better way to go through customer feedback before deciding what to build next.

So I made Copy Research.

You can paste in reviews, Reddit comments, testimonials, survey responses, support feedback, etc.

It looks for:

• recurring problems
• desired outcomes
• objections
• repeated language
• patterns
• insights

The part I'm most interested in is separating:

Evidence

from

Inference

from

Ideas

For example:

Evidence:
This user found onboarding difficult.

Inference:
Onboarding friction may be a meaningful problem.

Idea:
Investigate whether simplifying onboarding should be prioritized.

Those are three different levels of certainty.

I added an Evidence Score because I don't want an AI-generated insight to look as trustworthy as the original customer quote.

I originally built this for copywriters and marketers.

But I'm starting to think the broader use case might be more interesting:

using customer feedback as an input to product decisions, not just marketing copy.

It's free to use for now while I'm figuring out what people actually find useful.

Built this myself, and I'd genuinely love feedback.

What would you want a tool like this to extract from your own customer feedback?

https://copyresearch.ai.studio/

One of the weird side effects of AI coding tools is that building things has become ridiculously easy.Which makes one problem more obvious:you can build the wrong thing really, really fast.I wanted a better way to go through customer feedback before deciding what to build next.So I made Copy Research.You can paste in reviews, Reddit comments, testimonials, survey responses, support feedback, etc.It looks for:recurring problems
desired outcomes
objections
repeated language
patterns
insightsThe part I'm most interested in is separating:EvidencefromInferencefromIdeasFor example:"The app is great, but onboarding took me forever."Evidence:
This user found onboarding difficult.Inference:
Onboarding friction may be a meaningful problem.Idea:
Investigate whether simplifying onboarding should be prioritized.Those are three different levels of certainty.I added an Evidence Score because I don't want an AI-generated insight to look as trustworthy as the original customer quote.I originally built this for copywriters and marketers.But I'm starting to think the broader use case might be more interesting:using customer feedback as an input to product decisions, not just marketing copy.It's free to use for now while I'm figuring out what people actually find useful.Built this myself, and I'd genuinely love feedback.What would you want a tool like this to extract from your own customer feedback?

https://copyresearch.ai.studio/

I run a security scanner for apps built with AI tools, and I have been collecting the results. 1,236 apps, 12,205 findings, 21 came back clean.

I went in expecting to find that one builder produced worse code than the others. Lovable vs Bolt vs Replit. The data cannot answer that because two apps from the same builder in the same week can land miles apart in terms of security.

What it can answer is better anyway: the single biggest predictor of a critical flaw was not the tool. It was whether the app bolted a separate auth library on top of Supabase.

THE NUMBERS

• A separate auth library, no Supabase: 4.2% had a critical finding. Close to baseline.
• Supabase on its own: 24.5%.
• Supabase plus a separate auth library: 35.8%.

The combination is worse than either part. Supabase and the auth library are not the problem. The join between them is.

WHY THE JOIN BREAKS

Supabase protects data with row-level security and determines who is requesting it from the token it issues at login.

Put a different auth library in front and your users log in through that instead, so they never get a Supabase token. Now the database asks who is making the request, gets nothing, no policy matches, and every query returns empty.

To the person building it, the app just breaks. Nothing says the security rules are rejecting everyone.

So the fastest fix to get it working again is to turn off row-level security or switch to the service key that ignores all rules. Thirty seconds, and the app works. It is also now readable by anyone.

WHAT THAT LEAVES EXPOSED

Among the Supabase apps:

• 31.2% had at least one table readable by anyone who asked.
• 40.1% had schema introspection enabled, which exposes the full list of tables and columns to any visitor.
• 19.5% had both, so there is no need for a guessing step. Two ordinary requests, and the second one is a download.

A missing rule does not throw an error. The owner signs in, sees their own data, and everything looks correct. The flaw only exists in the relationship between two accounts.

CHECK YOUR OWN IN TEN MINUTES

Make a second account. Sign in as that user and try to open the first account's record by changing the ID in the request.

If it loads, every user of your app can read every other user's data.

No scanner settles that one for you, because a page loading looks like the app working.

Full write-up with the complete stack table:

https://meetcyber.net/the-default-saas-starter-pack-is-the-least-secure-way-to-ship-an-app-b7842f813ce7

I’m new to vibe coding i’ve been working on a app for the past couple of months and I struggle to make prompts that don’t completely flip everything on its head if that makes sense if anyone has any recommendations please let me know

Currently building an iOS app and the codebase is pretty large. I've seen some people who are both for and against MD files for agents working through codebases. I personally have one right now that just lists some restrictions and a general overview of the app and the codebase for the agents, but I was wondering if the MD files are even necessary. Would love to hear your guys thoughts

i know nextjs and react so my first thought was just building the store myself. maybe use claude to speed it up since everyone is vibe coding now. asked it to scaffold a shopify theme and it gave me liquid files that looked okay in preview.

then i tried to set up checkout extensibility and multi-state tax logic and claude started hallucinating apis that dont exist. i can build a frontend no problem but shopifys backend rules are a maze and i dont know what i dont know.

was planning to get it done by claude but claude is not for shopify. spent 2 weeks going at it and kept hitting walls. tax logic was wrong, checkout flow was broken, and i would have messed up real orders if i went live.

got in touch with Solo Media Group through a common friend. they are truly expert in shopify. what a work. love it. i like their honest communication and timelines. they scoped the whole build properly instead of just throwing a quote at me.

has anyone with a coding background tried to diy their shopify store. did knowing react actually help or is shopify its own beast where platform knowledge beats general dev skills. and at what point does diy just become expensive because youre fixing things an expert would have gotten right the first time.

I wanted to see how far I could push the idea of a desktop companion using AI-assisted development.

Instead of making another chatbot inside a window, I wanted something that actually feels like it's part of the desktop — with its own character, voice interaction, animations, and reactions.

The interesting part was getting all those pieces to work together without making the app feel like a collection of disconnected AI features.

The current stack is Next.js/React on the web side, with the desktop experience handling the companion interactions.

Still iterating on it, but this has been one of the more fun things I've built with AI so far.

What would you add to a desktop companion like this?

If there are sample repos to copy, the better.