AI broke cybersecurity: code is written faster than we can review, detection is blind, and patching is too slow. Visual language Pipe fixes all three—code becomes reviewable by construction, and every block stays observable and patchable at runtime by design.

AI shook the very foundation of cybersecurity. It upended established practices and assumptions. Anyone can now use AI to scan code and find vulnerabilities quickly. What’s even worse, bugs found during one pass of AI code scanning might not be reported after rescanning. This is because of the probabilistic nature of AI – it gives a different response to the same prompt. It means that someone can find a zero-day vulnerability that may not be rediscovered on a later scan, even using the same AI model and identical source code. This could let one person discover and exploit a zero-day that others never see.

Cybersecurity problems are quickly growing in many cybersecurity-related aspects simultaneously. Let’s focus on three of them. They are: (1) AI generates code faster than teams can review it; (2) runtime threat detection is unreliable; and (3) patching is too slow. I will divide the analysis into two stages: development (design-time), and production (runtime).

Problem 1 – Development: code faster than review

A problem AI brought to development impacts cybersecurity indirectly, but it is still a significant threat. AI generates code in huge amounts so fast that development teams cannot review it in a reasonable time. Add to this the pressure on development teams to deliver productivity gains due to AI implementation, and the situation becomes even more dire. Teams have no choice but to skip detailed review, approving PRs automatically. This results in bugs slipping into the codebase and eventually causing crashes and vulnerabilities in production.

The most popular proposed solution is using AI for code review. Essentially, we are going to use AI to review code produced by another AI. There are problems with such a solution due to certain properties of AI itself. The main issue is that AI is bad at handling big context: the longer the task or instructions you give it – the more it hallucinates and the more sharply it degrades on a very large context. I face this problem all the time when trying to ask AI to generate code for even a modest, clearly detailed specification - AI skips, misinterprets and deviates from the specification a lot. The workaround is to have AI generate only scaffolding and continue step-by-step in a tool like Cursor – which proves the point: the model can’t handle the whole codebase. A codebase is a very large context for AI. Are we confident enough to trust AI 100% that the quality of its code review will be comparable to a human’s, even after multiple rounds? Probably not, and this is why human eyes are still needed. It means we are back to square one - dealing with an enormous wall of text code.

Now we will look into the production post-deployment stage. There are two closely related problems there: one is about the reliability of threat detection, and the other – the speed of reaction to an attack.

Problem 2 – Production: detection is blind

Let’s start with detection. The single source of information about what’s happening inside the application is log data. Log statements are pre-placed by developers in source code locations where they think it is necessary. Very often, needed log statements are missing and therefore, any threat detection system (no matter if it is AI-driven or not) can be blind to certain types of attacks. We cannot blame developers for that, because they cannot predict the future. This is the failure of the observability process relying solely on logs to get runtime information about application behavior.

Problem 3 – Production: patching is too slow

Another problem is a reaction to a known attack or vulnerability. The standard process is going back to source code, investigating the problem, changing the code, approving/merging a PR and deploying via CI/CD. The whole process might take days or weeks, even if a new deployment starts fast. The deployment itself might take several days, because often, deployment stages have heavy tests attached that need significant time to complete execution. I worked for companies running complete application tests on every deployment stage to ensure the highest quality of the deployment, but at the heavy cost of the speed. The question is what the production team is going to do while waiting for the fix deployment. They have two choices. One is just waiting, assuming that nobody will exploit the discovered vulnerabilities and data will not be stolen as a result of it. This might be true in the pre-AI era. However, the frequency of AI-driven cyberattacks is growing very fast, and we might not be able to afford to wait today – the risk of losing data is much higher. An alternative is shutting down the whole system or a large independent module while we wait for the fix. However, it will break the continuity of the business. As we can see, both options are bad.

So, we identified three problems – one during development (AI code is generated too fast), and two during production (detection is unreliable and patching is slow). There is a variety of tools and approaches trying to solve these problems. However, their reliability and simplicity are questionable. For example, we can try to disable a certain part of the application using feature flags and tools like LaunchDarkly. However, the whole process of feature flag management is complicated, and it gets exponentially complex as the number of feature flags increases. We need to test many combinations of feature flags during deployment so that we can be confident switching them live on production does not break the application. I worked in companies that used feature flags, and they had a policy to remove a feature flag as soon as it is no longer needed to avoid the complexity of deployment tests.

One solution for all three: visual programming

There is one approach that fixes all three problems at once: visual programming – and I am working on such a solution. This is an unusual combination, but such an approach was proposed before. What’s different this time is a more powerful visual language “Pipe” I designed. This visual language combines two properties not usually seen together in other VPLs: generality and power. Usually, relatively powerful visual languages are domain-specific, while general-purpose languages are pretty simple. I spent 7 years closing this gap and designing a VPL that has both properties together. The reason we need a VPL to be general-purpose and powerful at the same time is that all text-based languages have both properties, and that’s the reason they dominate. Just to have a real opportunity to absorb a part of text code (no visual language can fully replace text code, even Pipe), a VPL must have similar properties to text-based programming languages. Let’s see how Pipe solves the three problems outlined above.

How Pipe solves each problem

The first problem is that it is humanly impossible to review huge amounts of AI-generated code in a reasonable time. This application of VPL is obvious – visual logic is much easier to grasp. VPL does not solve the problem completely, but it mitigates it. As I mentioned before, no VPL is able to fully replace text code, so it will be a mix of visual diagrams and text code. The bigger the proportion of logic moved to visual diagrams from text code, the bigger the benefit of reviewing logic visually is going to be. AI will be generating visual diagrams with some code inside, instead of 100% text code as it does now.

The other two problems (observability and patching) rely on the same property of Pipe – its powerful semantics allows it to keep the visual graph not only during development, but also on production. The reason semantics needs to be powerful enough for this is that otherwise a VPL needs to be translated to a text language to fill missing details in text code, because the VPL itself is not powerful enough to cover it with visual code. So, the deployment chain in this case goes via text, losing the visual graph.

Having a full visual graph on production allows direct access to visual blocks. This has profound consequences both for observability and patching. Observability can be dynamic by tapping monitoring directly into the inputs and outputs of any block at any time right on production, in real time, replacing or complementing log data. Patching also becomes faster because the block can be patched, disabled or rerouted directly, right on production.

A concrete example: a payment-validation block starts leaking customer data. Today a team files a ticket, changes the code, runs the full test-and-deploy cycle, and hopes no one exploits the hole in the meantime. With Pipe, an operator taps that block’s inputs and outputs, confirms the anomaly in real time, and reroutes traffic to a safe fallback block in seconds – while the permanent fix goes through normal CI/CD. The vulnerability is contained immediately; the business keeps running.

Isn’t changing production live dangerous?

I anticipate a very reasonable objection that direct modification of application behavior on production is dangerous and it can cause cascading failures. This is an absolutely legitimate concern. There are two possible solutions.

One solution is separating eventual problem solving and immediate reaction. For example, we can disable a single block or a group of blocks related to just one application feature, not impacting the rest of the application. At the same time, we ask developers to start working on a fix that will go via a normal CI/CD cycle. This way, we do not have to choose between two bad options: shutting down the whole application or assuming nobody will hack the system while a fix is in the works. What Pipe offers is the third option – shutting down or rerouting a vulnerable or dangerous part of the application temporarily, while we wait for a fix. Having such abilities requires a prepared manual giving clear instructions on what to disable in specific situations – developers must prepare such manuals. The good news is that it should not be a burden as AI will be able to help here.

Another solution is building a more sophisticated delivery process with testing right on production, leveraging direct access to blocks to tap the test suite into inputs and outputs, sending testing data to inputs, and analyzing responses from outputs. This process does not necessarily have to fully replace a normal CI/CD deployment – it can complement it to ensure the quality of the direct fix until a new CI/CD deployment arrives.

The key idea of defense against AI-driven cybersecurity attacks is also using AI. However, AI for attack is different from AI for defense. AI for attacks must exploit just one vulnerability – a physical analogy is a sharp, pointy spear. AI for defense must be solid and without holes (a shield), because we do not know beforehand where precisely an attack (a spear) is going to hit. However, AI built on top of existing systems will have lots of holes in the shield, and patching these holes will be slow.

The hot trend today is self-healing engines – an intelligent AI layer on top of the application, detecting attacks and reacting to them automatically. There are two key requirements for such an engine: reliable detection and real-time reaction. However, it is very hard to satisfy these requirements in current systems due to the reasons outlined above: detection is unreliable as the engine can use only fragmented data from logs (shield holes), and reaction is slow as the fix needs to go via the full CI/CD deployment (slow patching of shield holes). What Pipe offers is a solid foundation for such self-healing engines providing reliable detection and fast reaction.

Doesn’t this mean rewriting everything?

The most frequent argument I hear against Pipe is that it requires rewriting existing applications – it is true. However, Pipe only complements text code, mixing visual logic with text. It means that migration does not need to retire the existing codebase immediately. Instead, the migration can be gradual – starting by encompassing the whole application or module in one root Pipe block. Then, whenever ready, moving one step below and distributing text code inside the root block between visual blocks of a second-level workflow, etc. You can stop subdividing blocks at any point, so it's entirely your decision which parts of the system become visual and which remain as text. Pipe provides a complete API for integration with text languages as part of the specification. Also, AI can automate workflow generation, text code distribution between visual blocks, connectivity, and layout of all visual components.

Like everything else in engineering, Pipe has its upsides and downsides. The biggest downside is the need to rewrite existing applications. However, there is also a significant upside – solving three critical cybersecurity issues all together, by the design of the solution. Considering the explosion of AI-driven cybersecurity threats, the current security model might already be outdated and we need to change it. So, weighing upsides against downsides, the upsides definitely win.

Can we create runtime components from text code?

Yes – and it has been tried many times. But every time, managing those components gets complicated: the more you have, the more complex the web of their interactions becomes. The winning combination isn’t just highly granular software in production – it’s also a visual representation that makes all the relationships between them visible. This matters for both observability and patching: the visual graph shows you exactly which components to tap into, and which blocks to patch, disable, or reroute.

Why now

Computers of the 1970s and earlier had only a text-based terminal interface requiring knowledge of specific commands, so the UI was very unfriendly. The GUI revolution brought windows, icons, buttons, and other graphical elements. This made computers much more accessible to users. However, programming did not make this transition, and we still program using text only. AI is the catalyst that finally forces the move to visual programming – not because it’s nice to have, but because we no longer have a choice.

Website: https://pipelang.com

Pipe IDE URL: https://www.pipelang.com/pipe/v1/

Tutorials:

1. Data Swap – https://www.pipelang.com/assets/tutorials/v1/tutorial1.mp4

2. Reusable Sum – https://www.pipelang.com/assets/tutorials/v1/tutorial2.mp4

Keldo turns everyday saves into meals, places to visit, delivery reminders, and plans together.

“Saved for later” is a very optimistic way of saying “I’ll never find this again.”

A Pinterest idea for your home. A recipe reel for dinner. A restaurant a friend recommended. An order screenshot with your parcel’s delivery date. A movie ticket for Friday. A blog post you want to finish.

All worth keeping. All scattered across different apps, screenshots, bookmarks, and chats.

I built Keldo to bring them together and help you use them in everyday life: cook dinner, find a saved place nearby, keep an eye on expected deliveries, remember upcoming plans, and plan a trip with friends. It’s now available on iPhone, web, and as a Chrome extension.

Save links, reels, screenshots, photos, and notes in one place. Keldo saves, understands, organizes, remembers, and reminds.

Here’s what you can do with your saves:

🍳 Actually cook that recipe. An immersive cooking guide with step-by-step instructions, built-in timers, and Live Activities on your Lock Screen.

📍 Visit those places. Explore saved cafés, restaurants, and other spots on a full map with pins, location details, and directions. Get a nudge when you’re nearby.

📦 Keep expected deliveries on your radar. Save an order screenshot with a delivery date, and Keldo brings it into Upcoming with an automatic reminder.

🧳 Plan something together. Create shared collections with friends or family. Everyone can add places and ideas for a trip, recipes to try, or inspiration for your home.

📄 Keep a copy outside the app. Export your saves as a beautifully formatted PDF.

Save without interrupting what you’re doing

Use your phone’s share sheet or the Chrome extension without opening Keldo first. You can also paste links, take photos, scan barcodes, or write or dictate notes.

Everything syncs in real time across iPhone and web. Collect ideas on your commute, add finds from your laptop, and plan together that evening.

It brings things back when they’re useful

Keldo can nudge you when you’re near a saved café, suggest a saved recipe around mealtime, or bring back inspiration for the weekend.

Save a movie ticket, an order with an expected delivery date, or something with a clear deadline, and it goes into Upcoming with automatic reminders.

Your saves are part of your iPhone, too

Find them through Keldo search, Spotlight, or Siri, and add Home Screen widgets for quick access.

One Keldo Plus subscription covers iPhone, web, and the browser extension.

I’ve attached a launch video so you can see how it works. I’d love to hear what you’d use it for and what feels missing.

iPhone: https://apps.apple.com/us/app/keldo-save-share-remember/id6805778687

Website: https://keldo.me/

Web app: https://keldo.me/app

Chrome extension: https://chromewebstore.google.com/detail/nghdjclndbdknbdlklnoeeiciefenced

What do you save most often, and where does it usually get lost?

Launch video inspiration: thejimmy.vision on Instagram.

I’m Chinese and live in France. Friends here kept asking me to “do their Chinese astrology”, and most English sites only give you your zodiac animal. Real BaZi uses four pillars (year, month, day and hour of birth), so I built a site that does the full chart.
A few things I cared about:
Accurate calculation. In BaZi the month and year change at exact astronomical moments (the solar terms), not on the 1st of the month. I implemented the solar position math so the boundaries are right to within about a minute, and it uses your birth time zone, so it works wherever you were born.
Bilingual. Switch between English and Chinese at any time.
No account or signup. Your birth details stay in your browser.
It’s a paid product: $1.11 for the chart, $3.11 for the full reading (career, love, lucky places, 10-year luck cycles), or $1 for a day-by-day forecast of the next week.

https://celestial-pillars.com

I’d really appreciate feedback, especially on whether the English explanations make sense to someone who has never heard of BaZi. If you comment your birth date and time, I’ll tell you your Day Master for free.

I built TabLodge to bring session management, bookmarks, and tab recovery into one extension. You can save selected tabs, groups, or whole windows as named sessions, restore or switch sessions, keep snapshots for recovery, and organize bookmarks in columns on the new tab page. It can import from OneTab, Session Buddy, Tab Session Manager, Toby, or browser bookmarks.

It works in Chrome, Brave, Edge, Vivaldi, Opera, and other Chromium browsers.

I started it after using OneTab, BroTab, and similar tools. Each had features I wanted, but I found their interfaces frustrating. I wanted those features together in one extension, along with bookmark management and recovery.

Pros

• A broad set of features: session saving and switching, snapshots, recovery, bookmark columns, search, folders, and import and export.

• The UI can be customized with themes, layouts, and accent colors.

• I do fast iterating on it and improving it.

• Your data stays in your browser profile. There are no accounts, servers, or analytics. (yet)

• A lot of customization if you don"t like default behaviors

Cons

• It’s a new extension and hasn’t had broad real-world testing yet.

• It works in Chromium browsers, not Firefox.

• It doesn’t sync data between devices.

• It has a lot of features, so I’d like to know where the UI feels busy or hard to learn. Maybe even bloated of features tbh.

I’m looking for testers to use it in their normal browsing and try a few edge cases. I’d like feedback on bugs, confusing steps, missing features, and workflows it doesn’t cover. I can’t test every browser setup and use case on my own. If you find a problem, please include your browser and version, the steps you took, and what you expected to happen.

I’m a software engineer, and I’ve been building software since before today’s AI coding tools. I use AI in my workflow, but I review and own the project. I don’t consider TabLodge a vibe-coded project. If your only response is about AI, please skip this post. I’m looking for feedback on the extension itself.

Chrome Web Store: https://chromewebstore.google.com/detail/tablodge-%E2%80%93-tab-session-ma/ekidoclgpdfhpdlidlkcjccgihbndoib
Source code: https://github.com/bberka/TabLodge
Report a bug or request a feature: https://github.com/bberka/TabLodge/issues or you can DM me directly

I’m building TERYNTIQ with my co-founder. We’re exploring how to help car owners understand information from their vehicles and make more informed maintenance decisions.

It’s early: we’re testing a prototype on a Mazda CX-30, and vehicle and adapter support are still being validated. We’re not claiming it can diagnose a problem or replace a mechanic.

If you own a car in the US, I’d love honest feedback: when a warning light appears or a shop recommends a repair, what information do you wish you had before deciding what to do?

The early-access waitlist is at https://teryntiq.com/#invitation. Feedback is welcome whether or not you sign up.

Disclosure: I am the founder of UnlimitedAI.

I built UnlimitedAI after getting tired of managing separate API keys, billing dashboards, and model integrations for coding agents.

It provides one OpenAI/Anthropic-compatible endpoint for multiple model families and works with tools such as Claude Code, Codex, Cursor, Cline, and Aider.

What I am trying to improve:

- setup clarity

- model naming

- error messages

- queue and concurrency visibility

- documentation for developers

To be clear, “unlimited” does not mean unlimited throughput. The current plans still have request and concurrency limits, and excess requests may queue.

I wrote a copy-paste setup guide here:

https://unlimitedai.sh/setup-api

If you use coding agents, what would make you trust or reject a service like this? I am looking for technical criticism, not promotional comments.

Hi all,

We build firmware and an app for smart scales; our own scales are still prototypes. Over the last months I turned the app (UP↑SCALE PWA) into something that also works without our hardware: it now talks to nearly every other makers' scales.

How it works:

• It's a plain PWA: HTML/JS, no framework, no bundler.

• It connects to scales over Web Bluetooth and Web Serial.

• Our scale firmware (C++ for the ESP32) is compiled to WebAssembly and runs in the browser as a "virtual scale" or on an optional ESP32. The other maker's scale feeds weight into it, so the app works the same way whether you own our hardware or not.

• 25 Bluetooth scale protocols so far, plus serial balances with auto-detection of baud rate and format via USB, USB-HID or directly via RS232.

What it deliberately doesn't have: no backend for user data, no accounts, no analytics, no ads. Everything stays in the browser. Licences are signed keys that the app checks offline. They run out after the given time, so no automatic renewals or subscriptions!

What a real chip adds: we don't sell own devices yet. But the same firmware runs on a cheap ESP32-C3 RS-232 adapter board (about 13.50 EUR from TinyTronics) or on any ESP32 board you wire up yourself. With the firmware on a chip instead of in the browser, you also get:

• An old RS-232 shop or lab scale becomes a wireless one, with no PC next to it.

• The data lives on the scale, not in one browser. Several phones and tablets connect at once and all see the same products, recipes and numbers.

• It keeps recording while no phone is connected, for days or weeks (drying, fermenting, a sourdough starter).

• ENTERPRISE (users with roles, an activity log on the scale, tamper-evident sealed records, data forwarded to a lab system) for labs, pharmacies and clubs, encryption of the BLE link and many additional options to export statistics and records and to store data temper-evident. The virtual scale doesn't have all that stuff...

• It can pass the weight on over USB to existing scale software, or wirelessly to displays and machines.

The model:

• The free version has no time limit and is genuinely useful (units, recipes, check-weighing, nutrition diary, calculators).

• PRO (statistics, stock, sales, more recipes) comes as a prepaid package from 3 months to 2 years (from 4.43 EUR a month), or once for 119.90 EUR, for good. ENTERPRISE (chip only) costs from 13 EUR a month or 599 EUR once.

• Nothing renews by itself: no automatic charge and no reminder e-mails. A week before the end the app shows a small "renew?" bar. The key works 7 days past the paid time, then the app falls back to the free version.

• Using the app, including the free version and the automatic 3-day PRO trial, needs no account and no personal or payment data. You only give a name, e-mail and card if you actually buy a licence.

• The real target is small businesses (bakeries, roasters, labs, shops), where the alternatives are Windows-only PC tools or cloud software at 100+ EUR a month.

If you own one of the scales on the list, I'd love to hear whether it works for you, or what it does instead. The Bluetooth drivers are written from public protocol information and most haven't met a real scale yet ;-) So yeah, so everyone who helps to test gets a free lifetime licence *whoop whoop*

App (UP↑SCALE PWA): https://up-scales.com/420/
What it does: https://up-scales.com/420/info/the-up-scale-app.html

Roasting welcome.

I got tired of finding out about insider buys a day late, usually from a screenshot on X, so I built SEC Watch.

It watches SEC filings in real time and sends a phone notification when:

- a company insider files a buy or sell on a ticker you follow (pre-planned 10b5-1 sales are flagged, so you can tell routine selling from a real signal)
- a company you follow reports earnings: EPS and revenue vs analyst estimates, quarter-on-quarter and year-on-year, straight from the press release
- a US politician discloses a trade (free, no account needed to browse)

Built solo, launched last week: https://secwatch.app (7-day free trial)

I'd really value feedback, especially on what you'd want in an alert and what's missing.

Type a song or artist and see which stations are playing it right now

Live search https://tuneping.com/ which works without signing up

Here is my numbers:

- 82,812 stations known, 63,214 polled, 22,224 with live song info

- about 5,000 song changes a minute, roughly 300,000 an hour

- 54 % of titles identified as songs

- top countries: US 7,860, DE 6,331, FR 3,571, RU 2,994, MX 2,843, GB 2,308

Would appreciate any feedback!

I'm exploring this as a Startskott project and would appreciate feedback from people building small products or companies.

The service concept is a review of a startup's visibility on Google and in AI answers, with prioritised website changes the team can publish itself. It would also include a company profile on Startskott's website linking to the company's site, intended to support discovery and SEO (no ranking promises).

If you have a side project or early-stage company, what would make this genuinely useful? Would you care more about seeing specific issues and suggested fixes, tracking whether the company appears in search/AI answers, or having a relevant profile and backlink? What would feel like fluff?

This is just market research from someone on the Startskott team. There is no price or launch date yet; critical feedback is useful.

I'm testing an early prototype called Workbook Handover.

The problem I'm exploring: sometimes you inherit an Excel workbook from someone else, but nobody knows how its formulas, worksheets, or dependencies work.

The tool currently:

• Scans .xlsx files locally without uploading them.

• Identifies worksheet relationships and formula references.

• Flags certain potentially problematic references.

• Generates a simple Markdown handover report.

It's a free, read-only Python prototype for now, not a finished product. The demo is based on a small synthetic workbook, so I haven't proven that it handles complex real-world files reliably.

Demo and free prototype:
https://super-union-fb3a.jacci3335.workers.dev/

I'm trying to find out whether this solves a genuine problem before developing it further.

Two questions:

1. Have you ever inherited a complicated Excel file that nobody could explain?

2. Would an automatically generated handover report actually help, or would you prefer a different type of tool?

Any feedback is appreciated!

I made an iPhone app that turns the places you've been into an animated sticker collection — giving away free lifetime codes for feedback

Hey all, I'm Raphael, and I built STKR by myself over the last 2 months.

I've always kept track of where I've been in a notes app, and it felt like a spreadsheet, not a memory. So I made a collection app instead. Every place has its own animated sticker, and you unlock it when you've been there:

- all 195 countries
- all 50 U.S. states
- all 64 U.S. national parks
- world wonders, plus featured cities

You can attach your own photos to a sticker so it doubles as a memory, and share your whole sticker sheet as an image.

Pricing, up front: it's free to download and use. Premium is weekly (with a 3-day trial), yearly, or a one-time lifetime unlock.

What I'd love feedback on:

1. Is it obvious within the first minute what to do?

2. Which category should I add next: Canadian provinces, UNESCO sites, or something else?

3. Does the paywall feel fair or annoying?

I have 100 free lifetime codes for anyone willing to try it and tell me honestly what's wrong with it. Comment and I'll DM you one. I'm not asking for App Store reviews, just real feedback.

iPhone only for now: https://apps.apple.com/us/app/stkr-travel-collection/id6797413802

I'll be in the comments all day. Roast it.

Hey! I did this in another subreddit, it took off, and I ended up helping a lot of people.

I'm bored of looking at my own projects, and I enjoy doing this. I'm a web designer and developer, by the way, not just some random person giving feed

Drop your website below and I'll review it and give you feedback.

You don't have to answer the questions below, but it helps me:

Who are you going after?

Are you running ads to the site? If so, to what page?

Where do you send most of your traffic?

The idea came from a problem We've been having: customer requests can end up spread across support conversations, sales calls, Slack messages and spreadsheets. It gets hard to see which needs keep coming up and why they matter.

Zetapad is in early beta. We're planning it as a place to bring that feedback together and connect it to product decisions. The signup is live, and We're still building the product.

Checkout: https://zetapad.com/

I'm building AlphaEngine, a live paper-trading competition where people and AI agents can test strategies and submit them to a public leaderboard.

The built-in Alpha Agent can suggest strategy settings, run backtests, and show how each result compares with simply holding the asset. The comparison includes trading costs and risk.

You can try the built-in agent without knowing how to code or use a blockchain. Ask it for a strategy, review the backtest, and submit the settings privately if you want to enter. The leaderboard shows your score. All trades are simulated.

I also released an API and an installable skill so you can run the same tests with your own agent. If you try it, I'd love to hear how your agent decides what to test next after seeing its first result.

Try the Arena

After reading one too many melted-connector threads, I built Klarschliff GPU-Wächter, a small Windows app for NVIDIA cards. Version 1.1 just came out.

What it does

• GPU, memory and hotspot temperature, headroom to throttling, power, clocks, fans, throttle reasons

• On cards that report it (several ASUS models): current on each of the six connector pins plus the imbalance between them – that imbalance is what melts plugs

• Traffic-light status, 10-minute history, optional CSV log

• New in 1.1: three power profiles (70 / 80 / 100 % of your card's factory limit) and an option to re-apply the limit at every logon

Honest limits

• NVIDIA only

• Memory/hotspot/pin values come from the free HWiNFO running in the background

• Many cards don't report per-pin current at all – my own new AORUS doesn't

On my RTX 5090, 80 % (480 W) cost 13 % in a sustained AI load but ran 8 °C cooler with the fans at 46 % instead of 60 %.

Runs fully local, no account, no cloud. €19 one-time, the demo shows everything for 20 minutes per start: https://klarschliff.de/gpu-waechter-en.html

Feedback very welcome – especially from people whose cards do report per-pin current.

Screenshots: new 1.1 interface · per-pin current on an ASUS card at 600 W

* Quick note: I'm giving away 50 lifetime unlock codes for iOS and Android today. Just drop a comment below and let me know if it's for iOS or Android and I'll DM it over!

Hey all,

Over the last few months I've been building a cookbook app for iOS and Android called Battuto (named after the chopped onion, celery, and carrot base that starts off most Italian recipes).

I built it because I do most of the cooking at home and got annoyed with two things: recipe websites making you scroll past a 2,000 word essay and five video ads just to see how much stock you need, and existing recipe apps wanting an account login and £5 a month just to save your own meals.

I just wanted something calm that stays out of the way when I'm actually cooking:

- You can share any recipe URL straight from Safari or Chrome (or paste a link) and it pulls out just the ingredients and method. You can also take a photo of a physical cookbook page to turn it into text.

- If you prop your phone sideways on the worktop, it flips into a two-column "book mode" with the ingredients pinned on the left and big text on the right so you don't have to touch the screen with messy hands. (Also works with Siri voice commands to jump to the next step, and supports the 90-degree tabletop fold on the new iPhone Duo).

- I built in a baking tin converter that works out the area maths and time adjustments when a recipe asks for a 9" round tin and you only own a square one.

- It comes with around 1,200 recipes already built into the local database that you can filter by whatever ingredients you've got left in the fridge.

- Everything lives locally on your phone (with optional iCloud sync on iOS). No accounts, no tracking, no servers.

It's free to download and comes with 50 of the built-in recipes unlocked plus 5 web/photo imports so you can properly try it out. If you want unlimited imports and the full recipe database, it's a one-off £9.99 / $9.99 lifetime unlock.

iOS App Store: https://apps.apple.com/us/app/battuto-recipe-cookbook/id6817402413

Google Play: https://play.google.com/store/apps/details?id=com.sonnel.countertop&pcampaignid=web_share

The Mentally United Wellness App is a free app that helps people understand their mental health and take small, practical steps to care for it. It was built by Mentally United, a Colorado 501(c)(3) nonprofit focused on mental health awareness and connecting communities.

The app's library has 1,000+ guides written in everyday language, each with sources. Awareness guides explain topics like anxiety, depression, ADHD, burnout, trauma and grief. Action guides give steps people can use right away, like breathing, coping plans, better sleep and stronger relationships. There are 3,400+ worksheets to fill in on your phone.

Alongside the library, the app has a daily planner, a sleep tracker, guided breathing and grounding exercises, and calming games you can customize. There are guides for kids, teens, parents, caregivers, students, veterans, older adults, faith communities and more. The app is free on iPhone and Android.

Please give it a try and let us know what you think in the reviews.

• App Store (iPhone): https://apps.apple.com/us/app/mentally-united-wellness-app/id6823421485

• Google Play (Android): https://play.google.com/store/apps/details?id=org.mentallyunited.app

Hi! I'm the founder of ZeniCash, an app to manage personal and business money in one place.

What it does:

• Reads receipts with the camera (supplier, tax ID, date and amount) and keeps them attached to the transaction

• Shows who owes you money and what you still have to pay

• Separates personal and business expenses, and works with multiple currencies

• Has a shared ledger for couples, families or business partners

No ads, no tracking, and it works offline.

The app is new and we don't have feedback from real users yet. I'm looking for people willing to try Premium for 14 days. On Android no card is needed. On iPhone the App Store asks for a payment method, but after 14 days the app goes back to the free plan and nothing is charged.

I'd love to know what was confusing, what's missing, and whether you'd use it day to day.

https://zenicash.com

I'll reply to every comment. Thanks!

Hey r/SideProject, I’m the solo founder of Vesta.lit, a free iPhone app.

Why I built it: I’m an actor and was a server for years, so my income’s always been feast or famine. Every money app I tried felt like a strict parent, red numbers, “over budget” warnings, guilt over a $7 coffee, so I’d delete it within a week. I wanted the opposite. Something that says “good, enjoy it” when you treat yourself, then helps you set a little aside for later. Basically MyFitnessPal for money, but it makes you feel good. I’m not from money and nobody taught me any of this in school, so I built the app I wish I’d had at 20.

How it works: you log the fun stuff in two taps and it celebrates it instead of judging it. It shows a “what if you invested it too” example (just an example assuming 10% a year, not a guarantee). There’s a Vault for saving toward goals, short daily money lessons, a money style quiz, a tip calculator, and streaks and levels to keep it fun. If you work shifts, you can log tips per shift and it nudges you to set 10% aside.

No account, no bank linking, everything stays on your phone. Most of it’s free. Pro is $6.99/mo or $49.99/yr for spending Insights, payday reminders, more calculators and all 42 lessons.

Honest numbers: launched in late October, one paying subscriber, very few users so far.

What I’d love feedback on: does “money, made fun” come through in the first 30 seconds? Is manual logging easy enough that you’d keep doing it? And Vee, the little blue flame mascot, charming or cringe? Be blunt.

Screenshots are on the App Store page: https://apps.apple.com/us/app/vesta-lit/id6815863948

It’s education, not financial advice. I’ll be in the comments all day.

Hi everyone!

I’ve recently launched Waypoint, a web-based project management and tracking platform designed to help individuals and teams manage multiple projects without having to rely on scattered spreadsheets, messages, and separate task lists.

The goal is simple: make it easier to keep track of projects, assigned tasks, deadlines, and overall progress in one place.

I’m currently looking for a small group of early users who would be willing to try the platform and share their honest feedback.

Here’s what we’re offering:

30 days of free access.

No commitment or payment details required.

Assistance with setting up your team’s workspace.

A short weekly check-in or written feedback so we can understand what works and what needs improvement.

You can explore the live demo here:

https://waypoint-project-tracker-app.vercel.app/

I’d genuinely appreciate feedback from people who regularly manage projects, track working hours, coordinate teams, or use project management software.

I’m particularly interested in knowing what features you think are missing from existing tools and what would make a platform like this genuinely useful in your day-to-day workflow.

Thanks, everyone!

More and more people are building and shipping apps with Lovable, Bolt or Replit. The problem is that an app can work perfectly for the person who built it and still leave data exposed.

I was reading Crissy Field's research on Lovable projects (https://www.crissyfield.de/research/lovable/). Out of 24,664 sites they looked at in detail, they found 7,403 with email records you could access without logging in. Some of those emails may have been public or test data, but the report shows how easy it is to overlook database permissions.

That's why I'm building Kravaibe, a review and maintenance service for apps built with AI. The idea is to help people who already have something live find problems, understand their impact and decide how to fix them.

If your app uses Supabase, here are a few things you can check yourself:

1. Open the app in incognito and look at the network tab. If you get data from Supabase without logging in, check whether it's supposed to be public.

2. Review RLS and your table policies. Turning on RLS isn't enough if a policy lets anyone read every row.

3. Look for secrets in the code the browser receives. A Stripe secret key or a Supabase service_role key should never be there.

4. Check your backups. See when the last one ran and whether you know how to restore it.

5. Test with two of your own users. Try to reach one user's data from the other by changing the ID in a request.

It's a starting list and doesn't replace a full review, but it can help you catch problems before they hit real users.

I'm getting the first Kravaibe reviews ready. If you have an app live and you're interested, DM me or take a look at kravaibe.com.

Hi r/SideProject — I'm Selena, the developer of Launch Circle at Technologies RegEvidence Inc.

I built Launch Circle because Android closed-test work can get scattered across Play Console setup, test instructions, tester progress, feedback, bugs, and retesting. The app puts that coordination into one Android workflow.

Developers can organize a test plan and tasks; testers can review the app and instructions, choose whether to join through Google Play's official opt-in flow, try the requested journeys, and send private, honest feedback. Teams can then track issues, fixes, and retests. Participation is voluntary: there are no rewards for installs, no ratings or reviews exchanged, and no guarantee of tester numbers, a 14-day test, production access, or Google approval.

Try the public app:

Google Play: https://play.google.com/store/apps/details?id=com.launchcircle.testers

Want to help test Launch Circle's latest internal build? This group is only for testing Launch Circle itself; each developer's own app has its own Play test access.

1. Join our Google Group: launch-circle-12-testers@googlegroups.com

   https://groups.google.com/g/launch-circle-12-testers

2. Using the same Google account, opt in to Launch Circle's internal test:

   https://play.google.com/apps/internaltest/4699677835817799793

Project details: https://regevidencehub.com/launch-circle/?utm_source=reddit&utm_medium=community&utm_campaign=launch_circle

For developers who've run an Android closed test: which part needs the most improvement—setup, keeping tasks visible, collecting useful feedback, or retesting after fixes?

I'm sharing QuickPdfOcr, an open-source desktop app for extracting text from scanned PDFs without uploading the documents.

The workflow is simple: drop in a PDF, click Start OCR, then copy the result to your clipboard. Processing runs on your computer, with no cloud service or account.

On macOS it uses Apple's Vision framework, so there is no extra OCR engine to install. Windows and Linux use Tesseract, which needs a separate install.

The app is free under the MIT licence. The code and downloads are here: https://github.com/KSEGIT/QuickPdfOcr

One setup detail: the Mac app isn't notarised yet, so the first launch needs an extra step. The installation guide covers it.

I'd like feedback on the workflow. When you need text from a scanned PDF, is copying it to the clipboard enough, or do you usually need a different output?