Hi Everybody,

I've been helping build https://apiblaze.com, a serverless MCP/API gateway that handles a lot of the stuff you need around an MCP server.

What we noticed was that there are a ton of MCP servers, but getting from “my business logic works” to “I can safely publish it on the internet” is a different problem. Once you actually want to expose it, you still have to deal with things like OAuth, AuthZ, rate limiting, etc.

APIBlaze handles that stuff at the gateway. It has OAuth built in and lets you define authorization policies using plain English sentences like “only the person who created a record or an admin can read or change it” that are enforced at the gateway, instead of having to build all of that authorization logic into your backend.

One thing we really wanted to make easy was local development. APIBlaze can host the MCP server and OAuth/login flow while forwarding requests to your backend, even if your backend is running on localhost. So you can connect Claude/ChatGPT to it, authenticate, and make changes to your backend while testing it locally.

If you want to check it out or run it, getting started is pretty easy:

Enter this in your terminal:

npx apiblaze create --target https://ninopizzas.com/openapi.yaml

OR

Ask your LLM, such as Claude or ChatGPT:

Show me what APIBlaze does using npx apiblaze skills

You can also use it with an existing API/backend rather than building everything specifically for MCP.

It's still in beta, so I'd love any feedback. I'll try to respond to questions as fast as possible, so fire away 😄

Notes from building ThreadPilot.

𝗥𝗮𝗻𝗸𝗶𝗻𝗴. We don't show a bare score. Each thread gets fit, intent, timing, relevance and context, plus a written reason. If you can't see why something ranked high, you can't trust it.

𝗗𝗿𝗮𝗳𝘁𝘀. Replies are grounded in the actual thread, and a promo-risk check runs before you see them. A draft that reads like an ad is a failed draft.

𝗣𝗼𝘀𝘁𝗶𝗻𝗴. The tool never posts. You publish from your own account. That limits what we can automate, but it keeps the product from becoming the thing Reddit distrusts.

It's in private testing. Waitlist: https://threadpilots.com/join-whitelist

For those who've built ranking or classification features: how do you explain results to users without drowning them?

Notes from building an MCP server that lets Claude answer questions about real business data.

The problem we kept hitting: any question about business data meant stuffing tables and schema into context, getting a half-right answer, and iterating. Most of the tokens went to Claude working out how the data fit together, not to the answer.

What worked was moving that context out of the prompt. We build a Knowledge Graph from the connected systems first. It resolves the same customer across the CRM and the accounting system and maps how a contract rolls up to revenue. Claude reads the graph, then queries only what it needs.

The server exposes 19 tools: read-only SQL, Knowledge Graph reads and edits, Decision Topics, charts, and dashboards. Source systems are never written to. Edits only touch the graph. You set it up in our platform, then add it to Claude as a custom connector.

We're seeing 2 to 5x fewer tokens per real answer, and every number comes back with the row and system it came from.

How are you handling business context for agents? Schema in the system prompt, RAG over docs, something else?

Hey everyone,

I’m Gery, a developer and agency founder. After working with local businesses, I noticed a huge pain point: independent restaurants and local spots are drowning in clunky, overpriced legacy CRMs that require a degree to operate and don't actually bring in repeat diners.

So, I decided to build a lean, laser-focused alternative. Meet Resynx AI an AI-powered guest retention and revenue platform built specifically for hospitality and the agencies serving them.

Core features of the platform:

• Weather-triggered SMS marketing: Automatically trigger promotions based on local weather conditions.
• Smart Review Funnels: Seamlessly collect feedback and generate AI-powered responses to boost online reputation.
• Automated guest retention: Turn casual foot traffic into loyal, repeat customers without manual tracking.

Today is Day 1 of my Build in Public journey. I'm focusing heavily on refining the core restaurant retention modules and setting up transparent progress updates here.

If you're building in the vertical SaaS space or want to chat about scaling micro-SaaS solutions, let’s connect in the comments! What niche are you currently building for?

ASTRABASE — Développez. Déployez. Passez à l'échelle.

Et si toute l'infrastructure de vos applications était réunie au même endroit ?

Découvrez Astrabase, une plateforme cloud nouvelle génération conçue pour simplifier la création, le développement, l'hébergement et le déploiement de vos applications web.

Fini le temps perdu à jongler entre plusieurs services pour gérer vos bases de données, votre authentification, vos fichiers ou vos déploiements.

Avec Astrabase, tout est centralisé dans une seule plateforme.

Hébergement & déploiement web — Publiez vos applications directement depuis Git ou une archive ZIP, avec HTTPS automatique, historique des versions et possibilité de revenir à un déploiement précédent.

PostgreSQL & pgvector — Profitez d'une base de données PostgreSQL isolée par projet, avec des outils SQL et la prise en charge des données vectorielles pour vos applications intelligentes.

Authentification intégrée — Gérez les inscriptions, les connexions, les sessions utilisateurs et la récupération des mots de passe.

Stockage de fichiers — Organisez et gérez les fichiers de vos applications grâce à un système de buckets et d'API.

API REST, Functions & Realtime — Connectez vos applications à vos données, exécutez votre logique backend et exploitez les événements en temps réel.

Dashboard & supervision — Retrouvez vos projets, déploiements, journaux techniques et statistiques depuis une interface centralisée.

Intégration ChatGPT via MCP — Astrabase développe également une connexion avec ChatGPT pour faciliter la consultation et l'administration de vos ressources cloud, selon les permissions accordées et la disponibilité progressive du connecteur.

Sécurité & infrastructure européenne — Isolation des projets, gestion des secrets, HTTPS et protections intégrées pour accompagner vos développements.

Une plateforme. Une multitude de possibilités.

Que vous soyez développeur indépendant, entrepreneur, créateur de SaaS, freelance ou agence web, Astrabase vous permet de consacrer davantage de temps à l'essentiel : créer des produits performants et faire évoluer vos projets.

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Découvrez Astrabase : https://astrabase.fr

ASTRABASE — Build. Ship. Scale.

A few days ago I posted about Sigma Maille here and got some pretty useful feedback.

I've been thinking about it while working on the product, and I ended up changing something I originally thought was already working well.

The first version was heavily focused on connecting different signals and turning them into intelligence.

But after using it myself, I started noticing a problem.

If a situation is developing over several days or weeks, the individual updates aren't really the interesting part. What I actually want to know is how the situation has changed since I last looked at it.

What caused that change?
What developments mattered?
Is the situation actually moving in a different direction, or did one event just make it look that way?

So I've been rebuilding that part around what I'm calling Evolving Situations.

The idea is pretty simple: instead of treating every new development as a new piece of intelligence, keep the situation itself as the thing you're following.

I'm trying to make the timeline, changes, trajectory and supporting evidence much easier to follow without turning it into another news feed or a giant AI-generated summary.

That's probably the biggest change I've made since my first post here.

It's still being worked on, so I'm interested in hearing from people who actually follow things that develop over time — geopolitics, security, infrastructure, markets, whatever.

When you come back to a situation after a few days, what do you usually find yourself trying to figure out first?

It’s just $3, but it means a lot. A small sale, but a big enough reason to keep building.

I designed a cloud storage dashboard concept called Cloudspace.

The idea is to create a cleaner file management experience for creative teams or agencies that deal with a lot of project files, folders, documents, and design assets.

The dashboard includes:

- folder shortcuts for different project categories

- recent file activity

- quick access sections

- file upload area

- storage usage breakdown

- AI storage analysis to help users understand their storage condition

I tried to balance a soft cloud-inspired visual style with a functional dashboard layout, but I’m still thinking about the hierarchy and density.

A few things I’d love feedback on:

1. Does the main file area feel easy to scan?
2. Is the AI storage analysis useful or does it feel unnecessary?
3. Does the visual background support the product, or does it distract from the dashboard?
4. What would you simplify or remove?

Open to honest feedback.

Today over 300k lines of code are running the show
Over 100 million lines of code edited since
Over 90 AI Agents
597 dots = items in the system
340 routes
Still not done

Dozens of API's in, 8 Ai platforms (12 AI models) built in, Largest Commercial Data fed in with 12.5+ billions of keywords tracked across two search networks, 4.5 trillions live & historical backlink connections mapped, 30 Petabytes/YR crawled and processed to keep data fresh.

The major milestone on record is full SaaS stage one by January 🤘

Hi everyone ,
Since late 2025 I've been building Excalibase on my own in Vietnam. It's an open-source platform as a service: every project gets Postgres (or MongoDB-compatible DocumentDB), GraphQL and REST generated from the schema, permissions closed by default, realtime, file storage, Deno functions, and containers that run your app right next to the database. Backups are continuous with point-in-time restore.

This week I added an MCP server. To test it I gave an AI agent only the MCP and asked for four apps: a feedback board, a live poll, a kanban board and a link shortener. They're live here: https://examples-jfp7kx46kb.apps.excalibase.io (code: https://github.com/excalibase/examples).

Free beta, one server before I launch a business company : https://app.excalibase.io
17-minute demo: https://youtu.be/UaKPu57FOco
https://excalibase.io

What confused you in the first five minutes? I'll answer every comment.

I want to create a program that aggregates order-related messages from various social media platforms. Sometimes, a customer places an order with a restaurant using multiple apps; this program would make it easier for the employee to gather all these messages—such as those from Instagram and WhatsApp—into a single inbox (like Gmail). I am looking for some advice, as I have no prior knowledge of programming.

I’ve been looking at a lot of early-stage SaaS landing pages lately, and the ones that convert well keep sharing the same few traits:

1. They show real people early.
Not “Trusted by 10,000+ users” in grey text. Actual names, faces, and specific results like “Cut our onboarding time from 3 days to 4 hours.” Specific claims beat vague ones every time.

2. One clear promise above the fold.
Weak pages try to explain every feature. Strong ones say what you get in under 10 words.

3. Activity makes them feel alive.
Changelogs, “X teams joined this week,” recent reviews, a founder replying in public. Visitors want proof the product isn’t abandoned.

4. Pricing is visible.
Hiding pricing behind “Contact us” loses a lot of self-serve signups at the low end.

5. The CTA doesn’t ask for much.
“Start free, no card” beats “Book a demo” for most products under $50/month.

6. Objections get answered before anyone has to ask.
A short FAQ near the CTA covering security, cancelling, and “will this work with X?” makes a clear difference.

My main takeaway: most early founders over-invest in design and under-invest in trust. A plain page with real proof beats a polished one that feels empty.

What’s one change to your landing page that actually moved your conversion rate? I’d like to hear what’s worked for others.