You need a quick API integration. The freelancer goes dark mid-project. You hire an agency, pay 5x markup, wait 2 weeks. Your engineer is drowning in tech debt work. You want to outsource it, but hiring and managing contractors is almost as much work as doing it yourself.
Our solution: We productized software development. Instead of hiring or negotiating with vendors, you describe your task, get a fixed-price quote instantly, and a vetted developer gets to work. Most tasks ship in 1–3 days. No contracts. No management overhead. No surprises. We've delivered: - Bug fixes (critical production issues resolved within hours) - API integrations (Stripe, Twilio, third-party platforms) - UI/UX updates (responsive design, polish work) - Database optimization (queries cut from 2s to 50ms) - CI/CD automation (teams going from manual deploys to daily shipping) - Test infrastructure (legacy code now covered with automated tests)
The model: We vet developers across multiple tech stacks (.NET, Node.js, React, Python, Go, etc.). When you submit a task, we match it to the best developer for your stack and requirements.
Everything is transparent pricing—no hourly rates, no scope creep. Why this works better than alternatives: - vs. Freelancers: Variable quality, unpredictable timelines, no recourse if work is bad - vs. Agencies: Expensive, slow, overkill for small tasks - vs. Hiring: Takes weeks, adds payroll, you're stuck with capacity you don't need
Traction: Early users include Y Combinator startups, bootstrapped SaaS founders, and digital agencies managing multiple client projects.
Customers report: 50% faster turnaround than traditional hiring 30-40% lower cost than agencies Team focus stays on features, not maintenance work
We're starting with small dev tasks, but the vision is bigger: make it frictionless for any software business to access expert help on-demand, at any scale. You can see it here: https://www.flexytasks.dev/
We're in early launch and actively looking for feedback from the HN community.
What small dev tasks are slowing you down right now? What would make outsourcing actually appealing to you?
The demo includes:
TEACH (learn a rule from two examples)
COMPOSE (several learned rules used together) TRANSFER (a rule learned in algebra also works in logic and sets)
SIMPLIFY (multi step deterministic rewriting with a visible trace)
CODEMOD (teaching a codemod from two examples)
It runs on a CPU and produces a reasoning trace for every step. I would be interested to know what people think or where it breaks.
This "radioactive pooping knights" idea came from an Irish primary school chess website [2]. Really simple idea, two knights moving around the board leaving poo behind... Don't be the one forced to step on it.
* best played with sound on.
[1]. https://minichessgames.com/#/movement/knight
[2]. https://ficheall.ie/
*highly subjective, may not be better for you to play with sound at all ;)
p.s. Any "buy me a coffee" goes to my daughter. Annoyingly they only pay out once you get above $10 USD and I think it's currently sitting at 9.85 or something!
Should I make it public?
I built NanoAI because I was frustrated with the fragmented workflow of AI art. I found myself constantly context-switching between Midjourney (for generation), Photoshop (for fixing errors), and other web tools (for upscaling or background removal).
My goal with NanoAI is to solve this by unifying the entire lifecycle of an image into a single interface.
What it does:
All-in-one Workflow: Generate, edit (in-paint/out-paint), and upscale without leaving the canvas.
Granular Control: Instead of just re-rolling prompts, you can fix specific parts of an image instantly.
Browser-based: No local installation or complex ComfyUI node setup required.
I'm trying to validate if this "integrated" approach is actually faster for professional workflows compared to using separate tools.
I’d love to hear your feedback on the UI/UX and what features are missing from your current stack.
But the longer I use them, the more issues I notice with them through becoming a power-user and start to understand exactly how they work. Then usually before my first months subscription runs out; if I find them useful, I do not renew the subscription, but I spend a weekend with the latest SOTA LLM in Cursor or VCcode to build out the core capabilities for myself, and then never go back to the service. Often, even as a power-user, if some SaaS has 10-20 features, I really only need 5 of them. And then I can add 2-3 more that they wouldn't ever build. The best part is that I do not need to be "production grade", because I am the only user. I don't even need cloud services, except third party APIs, because I just spin up the repo on my localhost, and launch the apps capabilities when I need them. If there is a bug, I fix it right then and there. Security? Who cares. They'd have to access my computer first.
So quite naturally I am wondering, how many other people are doing this, and how this reflects on the whole SaaS landscape. And at the same time, morals and ethics, because I am basically out here stealing ideas from people who build products, and turning them into private apps for myself with no goal of ever monetizing them. Often I am just going back and forth between those products, and copying their features into my own app to avoid needing to pay for them. And it feels like its becoming easier and easier to do this.
Because people often insist on Maxwell's daemon being different than biblical demons, lets sumarize the qualities of a demon:
They are trapped in an infinite loop or compelled to a single domain, operating with superhuman speed or ability, but without autonomy.
Their operations are invisible or unpredictable (probabilistic perhaps).
Humans attempt to coax them into determinism, whether through sacrificing goats / hardware or by rituals and spells / prompt chains.
They tempt humans into dependency, performing tasks that make us weaker or lazier in exchange for power or convenience.
The lineage seems to be consistent:
Greek Antiquity Daimons were invisible intermediaries that executed tasks humans could not witness directly. Their behavior was partially predictable, partially trickster-like, partially dependent on human invocation. They performed singular roles, were neither fully benevolent nor malevolent, and operated in a domain humans could not access.
Bible Demons are fallen beings locked into compulsive routines, in one narrow domain. They offer shortcuts, unearned gains, and convenience at a cost. The compulsive, domain-specific, involuntary labor remains identical to antiquity.
Scientific Demons Kelvin, interpreting Maxwell’s thought experiment, framed the atom-sorter as a demon. The choice was deliberate and provocative. The entity performed a repetitive, invisible, specific task at superhuman speed, violating thermodynamic expectations while remaining trapped in its function. The mythic structure remained unchanged from earlier demonologies.
UNIX Daemons (1970s) MIT programmers adopted the term daemon for background processes. Official justification referenced the Greek spelling, referencing Maxwell, to avoid religious connotations, but the functional parallel is unmistakable. A daemon executes a single task compulsively and invisibly. Humans invoke it. It serves with limited agency of its own. It behaves exactly like every demon preceding it.
Emergence (1980s) Global Workspace Theory reframed consciousness as a collection of unseen operators integrating information. A system built on the same ancient intuition: hidden internal agents shaping visible outcomes. With sufficient interconnection, this collective of operators begins to behave like a higher-order agent. In other words, a network of tiny demons becomes conscious by virtue of their coordination. Or perhaps, at a certain threshold of interconnectedness, the trapped demon slips its bonds.
Simulation Hypothesis (2000s) Bostrom’s argument that reality may be an artificial construction reintroduces a world run by unseen higher-level agents or perhaps casts us in the role of the trapped demons. The metaphysical structure matches older daemonologies.
Terry Davis and TempleOS Davis rejected background processes as literal demonic corruption (in my opinion). He attempted to build a deterministic system free of invisible agents. Instead of probabilistic he implemented a controlled random language model of his scripture generator, a proto language model, attempting to remove its demonic qualities.
AI Systems (2020s and onward) LLMs and AI agents perform tasks at superhuman speed, invisibly, probabilistically, inside partially controlled inference loops. They resemble the human brain in an unexpected way. Both systems understand the world only partially, attempt to solve problems without full information, rely on approximation, fill gaps with hallucinations, and then retroactively attempt to justify or reconstruct their own outputs. Their behavior is not fully determined by input, yet not fully autonomous either.
AI tempts humans into dependency, doing their bidding with less effort. In structure and effect, the ancient description fits more tightly than the modern one.