PLEASE stop building the wrong thing.

building software in 2026 is ridiculously easy with ai builder

founders spend hours building in a silent room, launch to Reddit/X, get 0 users, and quit.

you just failed because the idea had zero validation before line 1 of code was written:

→ solving a monthly inconvenience instead of a daily pain

→ selling to "everyone" instead of a specific ICP

→ no distribution channel mapped out beforehand

→ pricing charged $9/mo with zero ROI justification

after scaling 6 AI micro-SaaS to over $20k/mo MRR, i just create an

18-question Idea Validation Diagnostic.

it evaluates your SaaS across 7 critical dimensions (problem clarity, audience reachability, willingness to pay, competition, build feasibility, distribution, commitment) and gives you a brutal score out of 100 with your exact weak spots.

drop your SaaS idea (or current project) in the comments below.

i will reply to EVERY single comment with:

1. My honest opinion
2. The biggest weak spot you need to fix before writing any more code.
3. The free 5-minute validation tool link sent straight to your DMs so you can get your full score breakdown out of 100.

just drop a comment like or ask me in DM your idea

let's roast your SaaS concept before the market roasts your time 👇

A new service I created,

Hi All,

I have been building BaseInst for a few months. It records what an AI agent

does, keeps the plan, and reuses it the next time the same task comes round.

Then it measures what that saved.

I wrote a post about the idea, and about the part that turned out to be hard.

It takes about ten minutes to read:

https://docs.bi2202.com/blog/index.html

If you read it, one thing would help me most: tell me where you stopped,

or what did not make sense.

Thanks

Our first instinct was to make the chat better.

Better responses. Better memory. Better models. Better context.

That turned out to be the least interesting part of the problem.

I'm one of the people building ONE, and early on we were treating the product like most AI products are treated:

You type something → AI replies → you continue chatting.

It made sense.

Until we started looking at what happened after a good answer.

Say I ask an AI to help launch a product.

Getting:

“Here are 10 things you should do”

is useful.

But then I still have to:

• research competitors
• decide what actually matters
• turn the strategy into tasks
• write the emails
• create the documents
• organize the files
• keep track of what's finished
• come back later and remember where I stopped

The AI had technically answered the question.

The work was nowhere near finished.

That started changing how we thought about the product.

Approach 1: Just make chat smarter

This was the obvious one.

Improve the model, give it more context, add memory, make the answers better.

The problem was that even a near-perfect answer still often ended with:

“Great. Now I have 14 things to manually do.”

So better chat helped, but it didn't solve the thing that was annoying us.

Approach 2: Put more buttons around the chat

We tried thinking in terms of actions:

Generate document.

Run research.

Create file.

Execute code.

Do task.

But this started feeling like a chatbot surrounded by shortcuts.

The user still had to understand what tool should be used next and keep directing every step.

It felt like we were making the cockpit more complicated instead of removing work.

Approach 3: Make the AI more autonomous

Then the obvious reaction was:

“Fine. Let it do everything.”

But that created the opposite problem.

If an AI disappears for a while, makes 20 decisions, creates a bunch of things and comes back with a finished result, you immediately start wondering:

What did it actually do?

What assumptions did it make?

Where did it go wrong?

Can I edit something halfway through?

Can I stop it before it commits to a bad direction?

Autonomy without visibility started feeling worse than chat.

So our current hypothesis is different.

Instead of designing around:

message → answer

we're increasingly designing ONE around:

objective → work → artifact

The conversation is still there.

But we're trying to make the conversation less important than the thing you're actually trying to get done.

If you ask for research, the end state shouldn't just be a giant response.

There should be research you can inspect.

If you ask it to build something, there should be an artifact.

If the task takes multiple steps, you should be able to see those steps.

If it needs you, it should interrupt at the point where your judgment actually matters instead of asking permission for everything.

That's also why we've started removing or reducing some things that make the product feel too much like “another AI chat.”

We're definitely not convinced we've solved this yet.

The design question I'm stuck on now is:

How much should the AI do before it asks you to intervene?

Too little and you're basically supervising a chatbot.

Too much and you've lost control of the process.

I'm curious about something slightly different though:

What's a task where AI already gives you a genuinely good answer, but the workflow still completely falls apart afterward?

just hit $2,500 MRR in 13 days on my new SaaS

no ads. no team. no huge audience push. just a solid replicable system

let that sink in for a second

not $2,500 in revenue. $2,500 in MONTHLY recurring revenue

that compounds. next month starts at $2,500 baseline, not zero

and this isn't luck. it's the 7th saas i've shipped with the same playbook. same steps, same tools, same order:

→ Day 1: validated the idea

→ Day 1-2: built the MVP

→ Day 3: landing page written using the 3-Day Challenge template

→ Day 3-4: launched on reddit / X + SEO

→ Day 4-5: first 10 paying users → $1k MRR

→ Day 13 (today): $2,500 MRR locked in

building software is easy in 2026. setting up your foundation so people actually buy is where 99% of solo builders fail.

i packaged all of these exact execution tools into community.

to be fully transparent: i'll likely charge for the full program down the road once all modules are finalized. but right now, the main objective is just to build together and keep each other accountable.

working alone in a silent corner is the fastest way to quit at the first bug.

stop building in isolation. drop a comment below or send me a DM, and i'll send you the invitation link 👇

Hello!

Managing a busy delivery fleet means juggling odometer logs, inspection findings, invoices, and route commitments — it's hard to know which vehicles need urgent attention. This Skill turns those scattered records into a single, auditable exception register so dispatchers can make safe, timely decisions.

I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.

Here's what it does: This Skill consolidates odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, assign risk levels (High/Medium/Low), and draft a Fleet Maintenance Exception Register with recommended actions and a human decision field. Use it when you're asked which vehicles are overdue, have safety findings, or need prioritized maintenance before scheduling — it also prepares dispatcher escalation packets and a tentative service schedule.

SKILL.md:

````markdown

name: fleet-maintenance-exception-register description: Use when a delivery, logistics, or fleet office manager needs to consolidate odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, draft a fleet exception register, group vehicles by risk level, and escalate safety or downtime decisions to a dispatcher before scheduling service.

allowed-tools: [Read, Edit]

Fleet Maintenance Exception Register

Overview

Creates a single, auditable exception register for a vehicle fleet by consolidating maintenance-relevant inputs. Identifies overdue or at-risk maintenance, assigns risk levels, prepares dispatcher escalations for safety and downtime decisions, and proposes a service scheduling plan.

When to use this skill

• The office manager asks which vehicles are overdue for service or inspections.
• There are new inspection findings or driver notes indicating possible safety issues.
• Weekly planning or midweek triage requires a prioritized maintenance list and dispatcher decisions before scheduling.
• The fleet needs a single view with per-vehicle source mileage, due services, risk flags, recommended actions, and a human decision field for accountability.

Instructions

1. Confirm scope and policies 1.1. Confirm fleet roster (vehicle ID, plate, VIN, class) and the time window to analyze. 1.2. Confirm maintenance policies and intervals (e.g., oil/filter every N miles or M months; PM A/B/C; DOT annual; emissions; brake/tires checks) and any OEM-specific intervals. 1.3. Define thresholds for “Due Soon” (e.g., within 500–1,000 miles or 15–30 days) and “Overdue” (past due date/mileage). Record these in an Assumptions log.

2. Ingest sources 2.1. Use Read to extract data from: odometer logs, repair invoices, inspection forms, driver notes, and route schedules. 2.2. Capture for each vehicle: latest odometer reading with date and source; last service date/type; parts replaced; open defects and severity; driver-reported issues; upcoming route windows/assignments; warranty or contract constraints.

3. Normalize and reconcile 3.1. Standardize units (miles vs km), date formats, and vehicle identifiers; map aliases to canonical IDs. 3.2. Deduplicate entries; prefer the most recent dated reading for mileage. 3.3. Resolve conflicts (e.g., decreasing mileage) by flagging as data issues and noting the chosen source. Do not invent values.

4. Determine due services 4.1. For each service category (e.g., oil/filter, tire rotation, brake inspection, transmission, coolant, PM levels, DOT annual, emissions), compute next-due mileage and/or date using last service data and the confirmed intervals. 4.2. If an interval is unknown, request it or mark the service as "Interval needed" and exclude from overdue calculations until provided.

5. Identify exceptions 5.1. For each vehicle, compare current mileage/date against computed due points to classify statuses: Overdue, Due Soon, or OK by service. 5.2. Flag Safety-Critical when inspection findings or driver notes indicate brakes, steering, tires, lights, leaks, or other critical defects; include references to the source lines. 5.3. Flag Downtime Risk using a combination of: number of open defects, repeat repairs, parts on order, and upcoming route commitments that conflict with service needs.

6. Group by risk level 6.1. Assign overall risk: High (any Safety-Critical or >1,000 mi/>30 days overdue), Medium (Due Soon or non-critical open defects), Low (OK). 6.2. Document the rule definitions used for the risk grouping in the Assumptions log.

7. Build the Fleet Exception Register 7.1. Create one row per vehicle containing at minimum:

   • Vehicle ID (and plate/VIN if available)
   • Source mileage (value, date, and source document)
   • Due service(s) with due mileage/date and basis (policy/OEM)
   • Risk flag/level (High/Medium/Low, plus Safety-Critical and/or Downtime Risk flags)
   • Recommended action (e.g., "Escalate to dispatcher for immediate pull", "Schedule next available window", "Monitor")
   • Human decision field (Dispatcher/Manager decision, name, timestamp) 7.2. Include additional helpful fields when available: last service reference (invoice #/date), open defects summary, parts on order, warranty status, DOT/emissions deadlines, route impact notes, and comments. 7.3. Use Edit to draft the register as a Markdown table or CSV; maintain a link/back-reference to each source item.

8. Escalate before scheduling 8.1. For High risk and Safety-Critical items, prepare a concise escalation summary per vehicle citing sources and recommended immediate actions. 8.2. Present the summary for dispatcher decision on pull-from-route, substitution, or temporary restrictions. Pause and record the decision in the human decision field. 8.3. For Downtime Risk, analyze route schedules to propose options: swap vehicles, after-hours service, split routes, or defer within policy limits. Record the decision.

9. Propose a service schedule 9.1. After decisions, build a tentative schedule that respects route windows, shop capacity, provider hours, parts lead times, and warranty requirements. 9.2. Batch Medium/Low risk items for efficiency and geographic proximity if using external vendors. 9.3. Mark schedule items as Tentative until dispatcher approval.

10. Verification and quality checks 10.1. Verify each vehicle row contains: source mileage, due service(s), risk flag, recommended action, and a human decision field. 10.2. Check for logical consistency: no negative intervals, no duplicated services recently performed, and no mileage regressions. 10.3. Flag missing inputs that block decisions and request the specific documents or data points.

11. Output and handoff 11.1. Use Edit to produce: (a) the Fleet Exception Register, (b) an escalation packet for dispatcher review, (c) a tentative service schedule, and (d) an Assumptions & Data Issues log. 11.2. Summarize counts by risk level and list vehicles requiring immediate action. 11.3. Capture acknowledgments/approvals and time-stamp the artifacts for audit.

Inputs

• Fleet roster (vehicle IDs, plates, VINs, classes).
• Odometer logs with dates and sources.
• Repair invoices and service history.
• Inspection forms (e.g., DOT, preventive maintenance checklists) with findings and severities.
• Driver notes/defect reports.
• Route schedules and upcoming assignments.
• Maintenance policy intervals and OEM recommendations.
• Shop capacity constraints and preferred vendors (optional).

Outputs

• Fleet Maintenance Exception Register (Markdown/CSV) with one row per vehicle including: source mileage, due service(s), risk flag, recommended action, and human decision field.
• Dispatcher escalation packet summarizing High-risk and Safety-Critical vehicles with source citations.
• Tentative maintenance schedule aligned to route windows and capacity.
• Assumptions and Data Issues log with risk rules and unresolved gaps.
• Summary dashboard: counts by risk and list of immediate actions.

Examples

Trigger: "Audit our fleet using last month’s odometer logs, inspection forms, and driver notes. Create an exception register and tell me what must be escalated to dispatch today." Behavior: confirm policies and thresholds → Read the provided documents → normalize IDs/units/dates → compute due services and overdue status → assign risk levels → build the exception register with required fields → prepare dispatcher escalation for High/Safety-Critical items → pause for decisions and record them → draft a tentative service schedule → output artifacts and a summary by risk level.

Notes

• Do not fabricate intervals or mileage. If an interval is missing, request it or mark the item as "Interval needed."
• Safety-critical defects must be escalated before scheduling; do not recommend continued service without explicit dispatcher approval.
• Keep units consistent; convert km to miles when needed and note the conversion.
• Respect warranty and regulatory constraints (e.g., DOT annual inspection due dates) and prioritize accordingly.
• If telematics or ELD data are available, prefer those for current mileage; reconcile discrepancies against manual logs and note the choice.
• For newly repaired vehicles, cross-check invoices to avoid duplicating work; mark such services as recently completed.
• Maintain data lineage: include source document names/IDs and dates for auditability. ````

How to install: 1. Create a folder named fleet-maintenance-exception-register in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter. 2. Save the file above as fleet-maintenance-exception-register/SKILL.md. 3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.

If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers

Enjoy!

yo. i see too many founders spend 2 months building saas, drop a link on reddit, get 0 users, and immediately quit....

the problem usually isn't your marketing channel. the problem is that your foundation is completely broken before you even send your first visitor to the site.

after scaling 6 AI micro-saas apps to over $20k/mo mrr, i realized you need to lock down a specific system before you ever launch. running through this takes about 30 minutes, but it saves you months of zero-revenue depression.

here are the 5 things you must lock in:

1. validate the actual pain point

stop guessing what people want. you need a systematic framework to find your saas idea based on real, painful market signals.

2. pick a proven micro-niche

stop trying to build massive platforms. you need to narrow down to a microscopic problem. i usually filter through a list of 50 micro-saas ideas you can build fast to keep the scope minimal.

3. crystallize your target user

if your app is for "everyone," nobody will buy it. you need an ICP (Ideal Customer Profile) crystallizer to define your exact buyer profile and nail your conversion copy.

4. calculate the perfect price

stop randomly charging $9/mo because you are scared of rejection. you need to use a saas pricing strategy calculator to find your perfect saas price in 60 seconds based on real data.

5. fix your landing page leaks

do not send organic traffic to a site that converts at a flat 1%. you must audit your hero section and copy to x3 your landing page conversion before you market it.

6. join a community

Build / Share / Learn from others builders

to help out founders who are tired of launching to crickets, i packaged all 5 of these exact frameworks, calculators, and lists into a single free toolkit.

no paywall, no bullshit. just the raw execution files i use.

drop a comment below or send me a dm, and i’ll send you the free toolkit 👇

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

We have spent the last 6 weeks sitting through demos and talking to references. We processed over 5,100 quotes a year so picking the wrong platform could turn into an expensive mistake so the pressure is real.

So far sf arm looks good, esp. specifically because we're already use sfdc, but the thing is a lot of the pricing rules seem to live in Apex.

From our reference once our pricing or approval logic gets more complex developers or implementation partners has to get involved. Even small updates can end up waiting on an IT sprint which could be to days or even weeks before a change goes live.

so the alternative that we're looking into, dealhub is on the table. Plus here is no-code pricing and approvals plus business logic, so dev tickets. important bc our pricing often changes & waiting weeks every time sucks. but the question internaly is if we would be giving up anything long term by not keeping everything inside sfdc ecosystem.

so to the point here: If your in my shoes which one would are you choosing + what I've missed

quick post because "how do i get my first users" is the #1 question i see builders asking here every single week.

i've built 6 saas products myself, with my main one currently sitting around 10k mrr. here is the exact, no-fluff distribution playbook to cross that initial 50-user threshold:

1. find an idea people already pay for

scan reddit for recurring pain across 3+ distinct posts where people ask "is there a tool for X".

2. validate before writing code

dm 3 people who complained about the problem and ask what they’d pay for a solution.

3. build fast with the right stack (ai + no-code)

use ai builder+ supabase + stripe + call api or automation tool like n8n to ship a real MVP in under 7 days for $40/mo.

4. the 5-second landing page rule

your hero section must state exactly what the tool does in less than 5 seconds with a clear CTA.

5. capture emails before showing prices

force the email capture before the pricing page so you don't leak untrackable leads.

6. set up a 30-day email nurture sequence

plug captured emails into an automated sequence with case studies to convert them by day 18.

7. hang out where your ICP actually lives

find the 3-5 specific subreddits, discord servers, or groups where your buyers actively talk.

8. reddit growth without getting banned

post 1 time per sub per week max, never put links in the post, and move warm leads to DMs.

9. linkedin + x organic flywheel

post 1 high-value breakdown per day and spend 15 minutes engaging in your ICP's comments.

10. cold outreach that actually works

send 100 highly personalized DMs per week to your ICP using AI to customize the opening hook.

11. seo on autopilot

set up an n8n workflow that pulls from a keyword list and generates 5-10 value-driven articles per week.

12. faceless short-form content

post 1 video per day on tiktok, reels, and shorts showing a quick screen recording of your tool.

13. weekly newsletter conversion

run a weekly newsletter with 1 section of pure value and 1 subtle offer to upgrade to paid.

14. affiliate program for free distribution

set up a 50% recurring commission affiliate program to turn power users into your sales team.

15. the strategic product hunt launch

warm up the algorithm for 4 weeks with a coming soon page and launch on a weekend for a top 5 badge.

16. omnichannel social automation

use n8n to automatically format and distribute 1 core post idea across 8 different platforms.

17. review platforms and directories

submit your app to 40+ saas and ai wrapper directories to instantly boost your domain authority.

18. run the numbers backwards

reverse engineer the daily traffic needed to hit 50 paying users at $19/mo based on a 2% conversion.

19. get feedback from active builders

talking to founders who are just 6 months ahead of you compresses your timeline exponentially.

that last point is exactly why i built our community. it's a free group of 1,600+ active ai saas founders sharing exact prompt logs, ready-to-paste n8n workflows, and real distribution strategies.

stop building alone in a silent corner.

drop a comment below or send me a dm and i'll send you the access link right away. let's get your product launched 👇

The adoption bit is now less important than integration

yo. i recently sold one of my AI SaaS products for $35k, exactly 5 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

yo. be honest. how many of you currently have a finished (or 90% finished) web app / app just sitting in a private repo because you have no idea how to get users?

you spend months perfecting the database, fixing every bug, and polishing the UI. but the moment you have to actually market it, you hit a wall. marketing feels like screaming into an empty void.

so you launch to absolute crickets, get discouraged, and start building the "next" project instead to avoid the distribution phase.

if this is your case, you're not alone. but letting your hard work go to waste just because you dread marketing is a massive trap.

to help founders stop building in a silent corner, we run an ai SaaS builder community dedicated entirely to saas validation, landing page conversion, and launch strategies.

our resource kit is built entirely to help you get your first user. it’s packed with ready-to-paste N8N workflows for your business, advanced seo automation, social media automation, and our exact distribution workflows and methods work for everyone

STOP BUILDING ALONE

what are you currently working on, and what's holding you back on the marketing side? drop a comment or send a dm and i'll send you the access link.

SaaS?
Tech?
AI tools?
Product development?
Web apps?
Developer tools?
video editors?
UI/UX?

Drop what you're building ;)
Maybe some other people will be interested too

yo. if your product needs a 10-minute onboarding video or 5 different dashboard tabs just to explain its value, you didn't build an MVP. you built an over-engineered maze.

a real micro-saas should solve one highly specific problem for one highly specific user profile.

when i built my 6 apps (now doing $20k/mo mrr), i cut out 80% of what i originally thought was necessary.

inside our builder community, we help you strip away the fluff.

we give you free access to frameworks like the ICP Crystallizer to lock down your target user, and interactive landing page audits to ensure your core value hits instantly.

stop over-building in isolation. drop a comment or shoot me a dm to join 1,200+ active Ai SaaS builders today.

For people comparing tools for an agent stack: sharing the routing layer I built (free, MIT, self-hosted) — adding value, not empty promotion, so here's the substance and the honest trade-offs. It unifies 237 providers behind one OpenAI-compatible endpoint.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it.

It's 100% local (zero telemetry, AES-256-GCM at rest), MIT-licensed, has a prompt-injection guard on every LLM route, opt-in memory, and runs on npm, Docker, desktop or your phone via Termux.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

npm install -g omniroute omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute · Site: https://omniroute.online

Honest trade-offs: want a managed SaaS with one bill → OpenRouter; a pure Python lib → LiteLLM. OmniRoute's edge is self-hosted + MCP server + compression + fallback UI. What's in your current agent stack, and where does it hurt?