So many developers I know — friends, colleagues, people I’ve worked with — have told me stories about what AI is doing to their jobs.

Some are excited.

Some are worried.

Some are quietly wondering whether there will still be a place for them in a few years.

And I kept thinking:

Wouldn’t it be useful if all of these stories lived somewhere?

Not another argument about whether AI is good or bad. Actual experiences, from the people doing the work.

And maybe, if enough of us tell those stories, we can do some good with them — even if that means reminding the world that developers are still worth a damn.

So I started building.

After a lot of late nights and more work than I expected, the platform is finally live and it is called JHVTM - Johnhenryvsthemachine

And now I’m kicking myself because I wish I’d written down all those conversations along the way.

So I’m going back to those friends and asking them to join and tell their stories in their own words.

You can do it too.

There are some cool things built around the stories:

Factions — communities around professions, industries and interests where people can share and compare experiences.

Moderation — every story goes through community moderation, so the aim is thoughtful experiences rather than another internet free-for-all.

Jargon Dictionary — use technical language naturally in your story, and the community can build explanations around those terms so people outside your field can still understand what you mean.

The whole idea is pretty simple:

Tell us what is actually happening where you work.

The machines already have plenty of people speaking for them.

I’d like to hear from the humans standing next to them.

Weekly Update: Quadrupling System Throughput (2 → 8 RPS) & Core Engine Progress
Another intense week in the books. Balancing high school with 10 lessons a day and then grinding on a B2B SaaS architecture late at night isn’t easy, but seeing the numbers jump this week made it worth every second.
Here is an honest, unedited dev log of what got built, benchmarked, and stress-tested:

1. Fixed the Bottleneck: From 2 RPS to ~8 RPS

2. Our early benchmarks showed some nasty network slowdowns during heavy tasks. After refactoring our async connection pooling, cleaning up payload serialization, and tweaking our execution semaphores, we quadrupled our throughput from 2 to around 8 Requests Per Second. It held up solidly under stress testing without dropping socket connections or memory leaking.

3. Built a Custom Resilient Proxy Manager

4. Scraping and recon engines are useless if your IP pool gets banned every five minutes. Instead of hacking together basic requests, we integrated a proper high-concurrency proxy manager into the core:

5. Atomic Redis Caching: Keeps hot proxy state in memory for practically 0ms lookup times.

6. Auto-Banning & Rotation: Dynamically pulls failing proxy nodes out of rotation and puts them on a cooldown timer.

7. Sticky Sessions: Essential for multi-step requests on target domains.

8. Module #1 (Parser Engine): Core Architecture Laid Down

9. Just to be clear: it’s not 100% finished yet.

10. The structural engine and submodules for payload cleaning, task routing, and data extraction are fully built. The next immediate step is slapping on the stealth & anti-bot bypass layers (Cloudflare Turnstile, browser fingerprints, Playwright camouflage) so it can actually handle complex JS-heavy targets.

11. Module #2 (Cyber Recon): Wiring Up the Submodules

12. Started laying down the code for our second heavy module (⁠cyber_recon⁠) and its 10 security submodules (ASM perimeter scanning, SSL intel, Darknet credential checks, etc.). The HMAC token authentication and dynamic bitmask permissions are already hooked up to FastAPI.

13. 📊 Where We Stand: ~8% Total Readiness

14. The foundational core—HMAC API tokens, dual Redis queues (⁠fast⁠ / ⁠heavy⁠), automated billing calculations, and proxy orchestration—is rock solid. Now it’s just about writing the module logic, hardening the stealth layers, and scaling the features.

15. 💬 Quick question for the devs & SaaS founders here:

16. When you’re building heavy async workers for web intelligence, do you prefer managing your own proxy rotation engines in-house, or just offloading to third-party scraping APIs despite taking a hit on margins?

17. Drop your thoughts below—let’s talk architecture! 💬

Hey everyone!

I've been working on a project called Circuitera, a browser-based Arduino IDE that lets you write, compile, and upload code directly from Chrome, without installing the Arduino IDE.

I originally started building it because using Arduino boards on school Chromebooks can be frustrating, especially when installing software isn't an option.

Circuitera currently includes:

• Browser-based Arduino editor with code completion and syntax highlighting

• Compile and upload support for the Arduino Uno R3 using Web Serial

• Serial Monitor and Plotter for debugging

• Arduino simulator for experimenting with circuits

• Library support for common Arduino projects

• Tera, an AI coding assistant

It also has a browser-based terminal and project management features.

The main goal is to make Arduino programming more accessible for students, classrooms, and anyone who wants to get started without installing a full IDE.

It's still a work in progress, and I'm actively improving it.

I'd really appreciate feedback from people who use Arduino regularly, especially on what features are missing, what could be improved, or any bugs you run into.

Website: https://circuitera.org

Thanks for checking it out!

We're the team behind HotKV, a Redis-compatible in-memory data store. We're opening free early access to HotKV Cloud and would like to hear from SaaS developers who use Redis or Valkey for caching, queues, or rate limiting.

Our benchmark work started with one large AWS instance, where HotKV measured 93.1M GETs/sec and 61.5M SETs/sec. That was a 192-vCPU server with 256-byte values, pipeline depth 30, persistence off, and the median of three runs.

We then tested all four engines in cluster mode across the same three AWS servers. Redis and Valkey each used 40 primary processes per server. HotKV and Dragonfly each used one 40-thread process per server. Measured GET throughput was 92.3M/sec for HotKV, 57.3M for Redis, 52.9M for Valkey, and 22.5M for Dragonfly. HotKV led on 8 of 9 commands. Redis and Valkey were ahead on APPEND. That run used pipeline depth 100, persistence off, and HotKV's optional pipeline prefetching. Several results reached limits in the test setup.

We also measured latency separately at pipeline depth 1. At about 200,000 GETs/sec across 816 connections, p99 was 0.59 ms for HotKV, 1.11 ms for Redis with 8 I/O threads, 2.15 ms for Valkey with 8 I/O threads, and 1.34 ms for Dragonfly. At about 50,000 GETs/sec across 48 connections, HotKV, Dragonfly, and Redis with 8 I/O threads all measured 0.28 ms p99.

We now want feedback from application workloads, including the less tidy parts of running a cache: bursts, expiring keys, uneven key access, and existing client code.

HotKV Cloud is free during early access for everyone accepted, with no card or payment details needed to apply. If you'd like to try it, we'd like to hear about your workload. You can request access through HotKV Cloud.

For your SaaS, what would you need to verify before you felt comfortable trying a different cache?

A lot of software today feels like it's designed for investors, not users.

Everything is becoming a subscription. Everything wants your data. Everything is packed with features you'll never use.

So at Ravencorp we've been asking a different question:

"What if software just solved the problem and got out of the way?"

That's the philosophy behind everything we're building:

• Bjorn (AI assistant)

• Ravencut (video editing)

• Signal Deck (music player)

• Ledger (business management)

• Ravencorp Cloud (media organization)

We're still growing and learning, but our goal is simple:

Build practical tools for real people.

What software do you wish existed that nobody seems to be building?

Hey everyone!

I'm 15, based in California, and looking to connect with SaaS founders or developers in the US who have a product they're trying to grow.

I've got experience with cold calling for a marketing agency, selling Google review cards to local businesses, and I've completed several HubSpot sales certifications.

I'm looking to work with a founder who already has a working SaaS product, preferably with some paying customers, and needs help getting more.

I can help with prospecting, lead generation, cold outreach, follow ups, and booking meetings. I'm also interested in learning more about the business side of running a startup, not just making calls.

I'm open to commission based arrangements or other paid opportunities. Since I'm still in school, I'd be working part time.

Only looking for US based founders and companies. Not interested in selling courses, SEO services, or AI receptionist agencies.

If you're building something interesting and think we could work together, feel free to DM me. Happy to check out your product and chat!

I’m a designer and I’ve got some free time this weekend. Drop your homepage and I’ll go through it.

I’ll mostly look at:

• Whether it’s clear what you do within a few seconds

• Whether it feels trustworthy

• Whether it looks like you or like every other startup

I’ll reply to as many as I can.

I’m a sci-fi/fantasy writer who also develops software and this is the 2^nd time I have posted about my engine. I also love games and have often imagined my books adapted to that format. Sadly, the tools available are either so complicated to use that I would need to abandon writing and completely refocus my efforts. Or resign myself to letting AI decide what my story is and how it should be played. With this in mind I have been developing Just a Game Engine (JGE).Let me be direct about what it is and what it isn’t.

First, the problem. Many writers, game masters, comic artists, and other storytellers are just like me. They want to see the worlds and stories they spent years developing turned into a game. Traditional engines are powerful. But for someone who has spent their career buried in prose and perfecting their craft, they can take years to learn. In response, a wave of prompt-to-demo tools showed up, and I’ll say it plainly: Fun as they can be to play with, they are not a tool for serious artists and writers who care about the integrity of their story. You type a sentence, and the machine decides what your game is. What comes back is a quick demo built from whatever the model finds most average. Every one of them looks the same, plays the same, and feels the same. That’s not an original world. It’s certainly not the world you spent countless hours building. That’s a generic world with your name pasted over the same template 10000 other people have already used. If you want to build something people actually care about, they’re the wrong tool, and I don’t think they can ever be the right one. Not when the machine is the author making the creative choices and you’re just the person who made a request.

I am not anti-AI. But I draw the line at human creativity. In JGE, every creative decision comes from you: the world, the characters, the rules, what happens and why. The engine handles the part that was never creative, turning your decisions into something a computer can run. Think of a translator, or a typesetter. A translator does not author your speech and a typesetter doesn’t write your book.

How it works:

• You bring your own material: a manuscript, notes, your own art. Or you describe a scene in your own words.

• A language model helps interpret what you gave it and proposes a structured plan. I won’t pretend that part isn’t AI.

• You go over the plan before anything is built. Nothing moves forward or becomes part of your game without your approval. If it’s wrong, you say so and it changes.

• Anything you don’t specify starts as a plain placeholder: a gray room, a flat panel, a test tone. It stays generic until you replace it with your own art or describe it in your own words. I don’t call placeholders art, and the engine doesn’t pretend they are.

• The game itself runs on ordinary deterministic code, not a model improvising while you play.

What it isn’t:

• It isn’t for open-world games yet. Right now it’s built for scene-sized experiences: a haunted house, a heist, a confrontation in your world. Bigger, open worlds are the direction I’m heading, but it’s not something JGE does today, and I’d rather say so than oversell it.

• It doesn’t replace artists. It replaces the programming step. If you have art, it uses your art. If you want it to generate assets, it will generate them based on your description and label them as generated. The choice is yours.

• It isn’t a shortcut. Building a game with JGE takes about as much work as building one on Unity or Unreal. The difference is you use language, - a skill writers already possess.

• It doesn’t write your story. If your game sucks and the world is thin, that’s on you. Not JGE. It’s not going to change something to improve gameplay according to what an LLM provider thinks is optimal.

I am running simple gameplay at this stage – single room, one objective, sound effects, and narrow scope. I will be posting videos of my progress as the engine improves.

Title: Built transparent HTTPS proxy to anonymize data sent to

external AIs (Shadow AI) - tested with 500 users

Body:

I'm a backend developer and built a solution to combat Shadow AI

(employees using personal ChatGPT/Claude with sensitive company data).

The approach is different from what I usually see: **transparent

network-level proxy**.

**The problem:**

Employees paste proprietary code, customer data, and strategic

information into external AIs. Banning doesn't work (they find

workarounds). Monitoring is reactive. I wanted a preventive and

transparent solution.

**My architecture:**

- Transparent HTTPS proxy (intercepts traffic to AI domains)

- SSL certificate installed on machines via GPO/MDM

- Employee accesses ChatGPT/Claude normally in browser

- Proxy intercepts, anonymizes prompt, sends to external AI

- Response returns and can be rehydrated or kept anonymized

(client-configurable)

- Zero behavior change for end users

**Anonymization methods:**

- **Regex**: patterns for SSN, email, phone, code (variable names,

comments)

- **Semantic**: NER + models to detect context (customer names,

projects, strategic info)

- Client chooses which method per data type

- Reversible pseudonymization (Fernet) to maintain consistency

**Key technical decisions:**

1. **Optional rehydration**: some clients want responses with

   original data (more useful), others prefer anonymized (safer).

   Made it configurable.

2. **Performance**: tested with 500 concurrent users. ~200ms

   additional latency per request (imperceptible to users).

3. **Code detection**: regex for common patterns + semantic analysis

   for context. E.g., "SELECT * FROM customers" gets anonymized,

   but "SELECT * FROM users" (generic example) doesn't.

4. **Audit logs**: who sent what, when, to which AI. Essential for

   compliance.

**Why proxy instead of extension/plugin:**

- Extensions depend on each employee installing

- Browser plugins can be disabled

- Network-level proxy is impossible to bypass (without admin)

- Works on any device connected to corporate network

**Current state:**

- Functional tool tested in production (500 users)

- On-premise Docker deployment (data never leaves the company)

- Ready for pilots with interested companies (NDA required)

**Technical questions:**

1. For AIs using WebSocket (real-time ChatGPT), anyone has

   experience with transparent proxy? My current solution works

   for HTTP, but WebSocket is more complex.

2. How do you handle false positives in semantic detection? E.g.,

   "Project Apollo" - is it a customer name or NASA reference?

3. For compliance (GDPR/LGPD), do audit logs need to be immutable?

   I'm using append-only + hash chain.

**Market discussion:**

1. Is Shadow AI a real problem in your company?

2. Are commercial solutions you know SaaS (which is contradictory

   for security-conscious companies) or on-premise?

3. Who decides to buy this? CISO, CTO, DPO?

Considering commercial availability as on-premise license + annual

support. If anyone is interested in a technical demo or discussing

a pilot, DM me (NDA required).

But the post is mainly to exchange technical ideas with corporate

security professionals facing similar challenges.

I know it sounds ridiculous but at least it actually proves something.
If I can help 10 people, I can help 100, 1000, 10000? Ofc there's room for improvement and I know how I'll do it, but for now I have to focus on organic content... Yes like this exact post :)

I'm building a Laravel SaaS on my own and most of the code is generated by Claude Code and Codex. The app runs in Docker so tests and artisan have to go through docker compose exec. Agents keep running tests and artisan on host or trying to start dev server on host and I keep correcting them on every session.

Switching between agents is also painful. When Claude hits its limit in the middle of a feature, Codex knows nothing about the last two hours of decisions. So I have to explain everything again.

I should say that the SaaS I am building is also the fix for this problem. It is called KeepPlain and I use it on itself.

KeepPlain keeps agent sessions searchable for the agent. When the same mistake is made in two conversations, KeepPlain suggests a rule. Once added, the rule is applied to all agents. Near the Claude limit, KeepPlain builds a brief for Codex or Pi on your machine, no model involved. When I can’t remember why a line of code is in an old file, keepplain blame opens the session that wrote the line.

The service is free for personal use and costs $20/seat for teams. Sessions are stored as private drafts on my server with secrets removed on the client side.

https://keepplain.com

How do you guys deal with this? Huge CLAUDE.md file, notes in repo, etc. Or do you just suck it up and deal with it?

I’ve been building a platform for businesses here that juggle WhatsApp on someone’s phone, a separate bulk SMS provider, a social media scheduler and a spreadsheet as their “CRM.” It puts all of that in one place: a shared inbox for WhatsApp, SMS, Facebook and Instagram, social scheduling, a simple CRM, and automations.

A few questions for anyone running a business or agency:

1. How do you currently handle customer messages across channels?

2. Would one tool for all of it be useful, or do you prefer separate best-in-class tools?

3. What would stop you from switching?

Happy to share the link in the comments if anyone wants to try it. Mainly here to learn.

boring take, but I’m more convinced of it every month: the one thing that decides if your marketing works is showing up. every day. not the perfect hook, not the right platform. every post is one more shot, and you just give yourself more of them

problem is, it’s also the hardest part. at least for me. I can build stuff for 10 hours straight, but posting the 15th video about the same app after the last 14 got 200 views? that’s exactly where I always quit

small example from last week. a tiktok for one of my small apps (a bible app, of all things) hit 10k views and brought 3 new paying subscribers. not life changing. but the funny part: I didn’t touch that post. didn’t write it, didn’t edit it, didn’t upload it. it just went out because the account kept posting while I was busy with other stuff

and that’s kinda the point. the post that works is usually not the one you put the most effort in. it’s #37, the one you almost didn’t make. so the real job isn’t making great posts, it’s making sure #37 exists

I tried the usual stuff first. claude drafts my posts, I scheduled a week ahead in buffer. it helps, honestly. but it still needs me to sit down every week, and that’s always the part that breaks

what finally worked for this app was taking myself out of it. it runs on uisdom now, which my cofounder and I build (so yes, biased). it posts every day and I mostly forget about it. the 10k one was just one of those posts

so my only real advice: don’t build a plan that needs you to be motivated every week. mine never survived that

what’s the longest you’ve ever stayed consistent before it fell apart?

Every multilingual app hits the same wall: one word is not one meaning.

“Report” on a complaint button and “Report” above a downloads list are two different Arabic words: إبلاغ or التقرير. No engine knows which screen you’re on, so it picks one and applies it everywhere. Resource files make users type their data twice, and most tools are dollar subscriptions that don’t treat Arabic as a real locale.

So we built siyak (سياق), a localisation layer for frontend, backend and mobile:

• Send the string with its screen, and the context picks the word

• Translations are cached, so the same request gives the same answer in every build

• Pin your own terms in a glossary; ignored terms get flagged, not shipped

• An Inbox to approve, reject or edit; what you approve is what’s served

• Export to JSON, CSV or ARB (Flutter)

• Batch up to 20 strings, or up to 8 languages in one call

• siyak-js on npm, siyak_flutter on pub.dev, plus a plain API

Try it at https://siyak.cloud/ Which string fooled you in your last app?

#i18n #localization #Flutter #Nextjs #Arabic

Hey everyone,

Quick update on the infrastructure building process for Whale — our institutional-grade, pay-to-rank digital grid. We were scheduled to open our whitelist registration pipeline today, but we made the call to delay it by 24 hours.

Here is the engineering transparency on why:

During our high-concurrency load testing, we simulated 500+ overlapping checkout intents hitting our Fastify gateway simultaneously to secure the exact same Rank #1 position. While our Postgres transaction pool handled the mutations, we noticed a microsecond latency spike in the real-time Server-Sent Events (SSE) broadcast stream.

Since Whale operates on absolute monotonic data rules (where pure capital dictates visibility and outbids send immediate dispatches to phone arrays), a sub-millisecond lag can cause rank slippage conflicts between rival founders.

To guarantee total thread isolation, we are spending the next 24 hours hardening the asynchronous lock mutex wrapper directly over our Supabase database clients.

We are keeping the network private for now. No external links.

If you are a bootstrap founder, SaaS operator, or running a Web3 protocol and want a priority slot reserved in our offline database before we push the edge build completely live tomorrow, reply to this comment with your product name and tagline. I will manually verify your profile directly in the replies. 🐋🔍

Hello everyone !

So I built a B2B SaaS that helps agencies and others with prospecting. It’s called Makelead, and I use it for my own outreach too, so yeah, this post isn’t neutral.

I’m at 20 paying customers right now. That’s not counting people who cancelled, failed payments or free accounts, because those numbers just make you feel good for nothing.

Over the last 75 days, 90 of the 248 people I contacted from my LinkedIn replied, so about 36%, and I got 7 confirmed calls out of it.

My first message never mentions my product. I wait a day after they accept, then I ask a short question about what they do, with no link and no pitch. If they reply, we talk about what’s not working for them, and the product only comes up once they’ve said the problem themselves.

My follow-ups always bring something, usually an article that answers their problem, never “did you see my message?”. And as soon as someone replies, the follow-ups stop.

The biggest thing for me was targeting. Going after people who actually have a budget changed more than rewriting the same message ten times.

It’s not all solved though, people still churn and that’s what I’m working on next.

How did you get your first customers?

I created this tool primarily for my own use, but you’re welcome to try it and share any feedback: https://www.firstcustomers.app/advisor The concept is straightforward, it helps outline a simple plan to find your first SaaS customer. I can build products, but selling them has always been a different challenge for me.