I’m doing short independent research on how small businesses handle enquiries from website forms, calls, WhatsApp, social media and referrals.
This is not a sales post. I’m trying to understand what works, what gets delayed, and whether existing tools already solve the problem well.
It takes around 2 minutes. Please do not include any customer details or private records.
Save some token ( money, but some water and electricity ) 🌳🌊⚡
using Interceptor : a MCP server that does part of the work before a api call is made , this allow to drastically reduce token usage sharpening the information sent to the heavy cloud model like Fable5, Sonnet5 , Opus .
try and let me know if it works for you!
Tip: if you see no calls to the tools, you may need to add a project rule to prefer the mcp server over the normal search tools. Enjoy
More info here: Interceptor
Builders-welcome post with the substance up front (disclosure: I'm the maintainer). OmniRoute is a free, MIT, self-hosted AI gateway — one OpenAI-compatible endpoint over 237 providers — built around two problems: runs dying on a provider 429, and tokens bleeding on tool/log output.
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.
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.
Fusion — an ensemble mode for the hard steps. Beyond simple routing, there's a fusion strategy that fans a single prompt out to a panel of different models in parallel and then has a judge model synthesize one best answer (mixture-of-agents, built in). It's cost-aware, so easy turns stay on one fast model and it only fuses when the step is worth it.
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.
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
GitHub: https://github.com/diegosouzapw/OmniRoute · Site: https://omniroute.online
Would value a critique of the routing/compression architecture from this crowd.
Situation: A construction company has an internal project management agent that needs to access weather data for better project briefings.
Question: Describe the technical steps and considerations involved in an agent invoking a tool, passing parameters, and processing the results, including error handling and state management.
here are my top 7 integrations that makes Hermes useful. here's my isolated setup to run the agent in the cloud it has the ability to keep working on something even after you've put your phone down.
Obsidian. Every note and backlink in my vault becomes context the agent can pull from. Instead of me copying notes into a prompt, it reasons across everything I've already written and connects things I'd forgotten I noted down.
Reddit. For market validation, this beats searching blog posts. Real users complaining about a product or comparing tools give you signal that SEO-optimized content buries.
GitHub. Code, issues, pull requests, all readable. This turns Hermes from an assistant into something that can actually look at your repo before answering a question about it.
YouTube transcripts. Feed it an hour-long podcast or conference talk and it comes back as searchable text within seconds. I didn't expect to use this much, and now it's one of the integrations I reach for most.
Google Workspace. Gmail, Calendar, Drive, Docs, Sheets, all through one connector. An agent that can't check your inbox or read your calendar isn't doing much. If you're only going to set up one integration first, this is probably it.
Stripe. Revenue, refunds, subscription changes, failed charges, all queryable instead of requiring a dashboard click-through. Ask "how many trials converted last week" or "which customers downgraded this month" and get a direct answer. It stops being a payment processor and starts acting like a business intelligence layer.
Twilio. Gives the agent a voice for real phone calls, booking reservations, confirming appointments, chasing invoices. I've listened back to a few call recordings purely because they're entertaining.
i use ai every day, it's part of my job. i feel guilty using it. there isn't enough sustainability content around the internet or maybe the algo don't push it, but it's harming the environment. the energy used to train the data, or run each query, adds up to a lot of litres of water. the water is used to keep the servers cool.
inference, just the everyday use of these models, is now burning more energy than training them ever did, and the user base keeps growing faster than anyone's fixing the footprint.
this isn't unsolvable problem. Companies could build more efficient models and run on cleaner power, they just don't, because it slows them down and costs more.
a few alternatives i've found that are actually built with this in mind:
what do you think of sustainable AI, something that uses less of carbon footprint?
I built an AI agent to manage Meta ads for my wife's small ecom store in Pakistan. Full access to the ad account via API. Monitor campaigns, track ROAS and ACOS, flag issues, recommend changes.
First real audit, it told me I'd spent 2.8M PKR in a period where Ads Manager showed 22k. Stated with total confidence, nicely formatted tables and everything. I only caught it because I know my own numbers.
Here's the part that changed how I think about agents. Instead of opening a workflow editor and tracing nodes, I challenged it in chat. Told it I didn't trust it. Then asked it to audit its own instructions and tell me what was missing. It came back with its own diagnosis: it was trusting raw API output without sanity-checking against my stated reality, reporting spend without campaign-level verification, and never flagging anomalies. It proposed adding a data integrity protocol and a "financial controller" role to its own system prompt. I approved, and that's now baked into its permanent instructions and memory, not just that one conversation.
It's not done. It still needs output validation before I'll trust a single number it gives me, and it fell over when I added browsing tools. But coaching an agent like a junior employee, and having the correction stick across sessions, feels fundamentally different from debugging a Zapier or n8n flow every time something drifts.
Question for people building agents on n8n, Make, or code: how do you handle an agent confidently reporting wrong numbers? Prompt-level guardrails, hard output validation against source of truth, or do you just keep a human in the loop forever?
👋 Cofounder of Era Finance here. We built an MCP server called Era Context that connects your bank accounts to whatever AI assistant you're already using (Claude, ChatGPT, OpenClaw).
Since agents need context and they don't have it otherwise, we thought this would be the right direction to keep our data private, but allow agents to tap into it.
Anywho - Era Context sits underneath or between the banks and agents. It connects to your accounts, cleans up transaction categorization (bank categories are bad), lets you set custom tags and automation rules, and exposes the right details over MCP so any connected assistant can actually reason about your real numbers. Plus, cross agent memory, tell Claude a goal, and it's still there when you open ChatGPT.
Things I'm genuinely unsure about and want pushback on:
Not asking for sign ups. Happy to answer anything in the comments, technical or skeptical. Shared the link if people want to poke at it. Can also offer dummy data to test our manual tools.
Not talking about building or demos. Talking about operating agents in live environments, across teams, with real business processes running through them.
If that's you — what are you running into day to day, and have you found anything that actually works?
The pain points I keep hearing about at this stage:
Two things I'm genuinely curious about:
Specifically interested in multi-agent setups and teams operating inside enterprise environments where compliance and accountability matter. That's a small crowd and Reddit might not be where they are — but worth asking directly.
Cursor / Claude credits finish incrediblly fast. I've tried launching two prompt in fable5 and my plan was already exausted. I decided to dig deeper and I found a way to save chunks of tokens in 2026
here is the Interceptor : a MCP server that does part of the work before a api call is made.
free demo : https://github.com/MXZZ/Interceptor-demo
Hey everyone,
I'm curious - what are the biggest problems you face while building AI agents (whether hand-coded or vibe coded)?
Could be anything: prompting, tool calling, memory, context management, debugging, deployment, latency, integrations, evaluation, or something else entirely.
Would love to hear what's been the most frustrating part for you.
Recently, I got a bad experience using Github Copilot that made me realize how funny this product's customer support works.
After using all credit for Copilot Pro+, I 've tried to upgrade to Copilot Max to avoid waiting 20 days to reset. Boom, after few times upgrading unsuccessfully, my card was charged $61 but my Copilot account hasn't been upgraded yet.
I tried upgrading again and my card was charged twice $61. GitHub even sent me 2 identical invoices for this upgrade.
I already created 3 tickets on https://support.github.com for this unreasonable charge and so far, over a week has passed with no response from GitHub.
Funny, huh? Has this ridiculous situation happened to you guys?
i've been running longer and longer agent workflows, and the hard part is no longer just writing the prompt.
it is orchestration, synchronization, and agent management: knowing what loop is running, what check failed, which agent needs attention, and how all the pieces fit together as one bigger system.
so i built `flows`.
-the basic idea is simple:
-agent blocks do fuzzy work.
-code blocks do deterministic checks.
the flow graph shows how all of them connect.
for example, you might have one agent loop trying to make JAX/GPU code faster. a Python block benchmarks the result. if the benchmark says `too_slow`, the code goes back into the optimizer agent. if it says `fast_enough`, the flow moves on to another loop, maybe memory optimization, then another check, then a final review agent.
the point is not just one agent plus one test. it is being able to run and inspect many agents, many checks, and many loops as one visible workflow.
goals are attached to individual agent blocks. code blocks decide whether a loop really passed.
the whole thing is written as markdown, so agents can read, edit, and operate on the workflow directly.
each `##` section becomes a block in the flow. YAML declares inputs, routing, loops, models, executors, max runs, and exhaustion behavior. normal markdown becomes the agent prompt. fenced Python/bash/etc blocks become deterministic runtime steps.
then the custom markdown runtime compiles that file into an executable graph.
there is also a local flowchart viewer for agent orchestration and live synchronization, so you can see the system as it runs: agents, code blocks, inputs, outputs, goal cards, loops, and live execution state.
i wanted something that felt closer to a notebook or org-mode file than a drag-and-drop builder, but still made long-running agent systems visible and debuggable.
I was finally able to obtain a discount link, this will give 50% discount on first month for new users, feel free to use it, it should work also for other models like Opus4.8
The product
PaperSweep (papersweep.eu) takes a batch-scanned PDF — the kind you get when you throw 200 pages into a scanner without sorting — splits it by document, OCR-reads and classifies each one, and returns a ZIP with categorised folders plus an Excel log. 16 categories, 8 languages, no account, no tracking, files deleted within 2 hours, EU servers.
The market
No clean TAM figure. The pain is real and widespread — anyone with a scanner and a document backlog. Primary focus: German-speaking Europe (DE/AT/CH), where paper bureaucracy is a genuine cultural condition. Competition is either general AI assistants (wrong workflow) or B2B extraction APIs (wrong audience). No consumer-facing, no-login, batch-scan-specific product exists in this space that I've found.
Stage
Live, functional, ~5 visitors/day. Not raising. Bootstrapped alongside a day job.
Conversion strategy
SEO/GEO, Reddit, cold outreach to German tax advisors who batch-scan client documents, eventual Product Hunt launch. This is the weakest part — happy to be told I'm wrong.
Why me
Built this because I had the problem myself and couldn't find anything that handled a mixed batch scan without a developer setup or US servers. The privacy-first approach isn't marketing — it's how I'd want to use it.
Roast targets
Does the landing page explain the product in 5 seconds? Is no-login a trust signal or a red flag? I have currently 16 hard-coded document categories and additional that AI suggests. Is there anyway better way to handle it that suits Europe's multi-cultural setting.
I’m releasing an open handoff draft of a framework I’ve been developing called the Thought Tree AI Framework.
At its core, the framework uses a simple pattern:
Data Units → Operations → Data Units
A Thought Tree program applies this recursively. Complex cognitive work is decomposed into named artefacts, transformations, contracts, modules and traces.
It came out of experiments with Auto-GPT-style agents, creative production pipelines and the need to separate what LLMs are good at from what deterministic code should handle.
I don’t currently have time to continue developing it properly, so I’m releasing it as an open handoff for anyone who wants to critique, fork, implement or reinterpret it.
The repo includes:
- a concise README;
- one-page summary;
- draft TTML schema;
- minimal example workflow;
- roadmap;
- original long-form explainer.
I’m especially interested in whether people see value in Thought Tree as:
- an intermediate representation for LLM workflows;
- a design vocabulary for structured AI production;
- a small open-source executor;
- or something that could map onto LangGraph / LlamaIndex / other orchestration tools.
Repo: https://github.com/RobertBateman/thoughttree-framework
Feedback, criticism, forks and maintainers welcome.
TLDR: Building Handoff, Better Authentication for AI Agents, sign up here to be a low-commitment beta tester: https://tally.so/r/NpgbvQ
Giving autonomous AI agents long-lived, unrestricted API keys to your tools and databases is an invitation for disaster. But how do you enforce least-privilege security without breaking the agent's workflow? That's where Handoff comes in! a secure, auditable MCP proxy that acts as a token broker for AI agents. Instead of passing raw credentials, Handoff: issues short-lived, revocable JWTs per tool/action, enforces strict, scope-based authorization policies, injects upstream secrets securely so they never reach the client, and maintains an append-only audit trail for compliance
Anyone is welcome to be a tester, you just need to have some interest in ai agents!
Interested in testing this out and giving me feedback, fill out this form: https://tally.so/r/NpgbvQ
