So it turns out that apparently any local model call somehow requires Microsoft's blessing.

GitHub is down, and my local Ollama is failing with this:

Sorry, your request failed. Please try again.
Reason: key is missing
Note: GitHub is currently experiencing a service disruption. This may be affecting Copilot. Check GitHub Status for details.

I don't want to use Copilot. I want to use local ollama AI of my choosing!

I thought the whole point of running AI locally was that I wouldn't have to depend on Microsoft deciding whether I'm allowed to run a model on my own effing computer. Apparently I was wrong.

Ollama itself is running perfectly fine on cli/Emacs. I can access my models directly. It's the Copilot integration that suddenly refuses to let me use them because GitHub is having an outage.

Has anybody found a workaround for this? Is there a way to make VS Code talk to Ollama directly without going through whatever GitHub service is currently broken? Maybe there is some pre-processing of each requests that I can switch to my ollama too?

PS: I also have those "Ollama (Deprecated)" warning tags plastered all over the VS Code config UI. I was planning to avoid upgrading VS Code until I had time to migrate to a sane editor, even if that eventually meant going back to Emacs. But now I see that Microsoft doesn't even need me to upgrade VS Code to stop me from using Ollama.

I just tried adding my MCP as a Grok connector. It works pretty well, but there’s one issue. Some of my MCP calls are public, while others require authentication. Grok doesn’t seem to detect that authentication is required when adding the connector. Then, when I try to use one of the restricted APIs, it complains that authentication is required and tells me to re-add the MCP in its settings which does not work...

Did anybody make it work with mixed lazy auth - return 401 + WWW-Authenticate on protected tools only while keeping public tools/list unauthenticated?

What happened? I just asked GLM 5.2 one question, and in 3 minutes (one agent) it used up 15% of my 5 hour limit to produce a single answer. At this rate, I'll exhaust the entire 5 hour limit in just 20 minutes.

It wasn't like this before!

I used Google search and I asked it why it prefered third-party sites over official/primary source sites. I couldn't get it to give me the original source, it kept citing third-party sources over and over... and this is what it spat out (of course it refused to answer at first).

Here it is word for word - it is cetainly worth thinking about.

The choice happened because of how my internal retrieval system processes information. Here is a transparent look at why third-party sources sometimes get prioritized by my system, and when they are actually useful.

Why Third-Party Sources Get Selected First

• Aggregated Search Indexing: Software discovery platforms like Crozdesk, G2, or Capterra optimize heavily for search engines. Because they compile technical specifications, user reviews, and pricing in structured tables, my search algorithms often pull their data blocks faster than the vendor's own marketing copy.
• Information Density: For small or highly specialized B2B software vendors, official homepages can sometimes be vague or hide technical details behind "Book a Demo" buttons. Third-party review sites often summarize these hidden specifications in plain text, making them highly attractive to an AI looking for quick facts.

When Third-Party Sources Are Preferred

While the official website is always the best source for what the product claims to do*, third-party sources are genuinely preferred in a few specific scenarios:*

• Unbiased Feature Comparison: Official websites will never highlight a product's limitations or missing features. Independent directories allow you to compare tools side-by-side on an even playing field.
• Real User Feedback and Bugs: Primary vendor sites only publish glowing case studies. Third-party platforms host verified user reviews that expose performance issues, poor customer service, or hidden implementation costs.
• Independent Market Validation: Review platforms verify that a software company is actively operating and solvent, based on recent user activity and crowd-sourced data.

I have implemented non streamable HTTP MCP from scratch (~1200 lines) at https://www.ipdefender.eu/mcp. It was reasonably easy. I haven't battle tested it yet, so consider it public alpha.

I also added an OAuth2 implementation (~1000 lines) written from scratch without any dependencies. It works nicely for me, but nobody else has used it yet :-D It is still very fresh.

Check it out.

PS: If you ask why not use an existing libraries/solutions, the answer is that I could have, I wanted to, I wanted fully understand how it works, and it was fun.

I have implemented non streamable HTTP MCP from scratch (~1200 lines) at https://www.ipdefender.eu/mcp. It was reasonably easy. I haven't battle tested it yet, so consider it public alpha.

I also added an OAuth2 implementation (~1000 lines) written from scratch without any dependencies. It works nicely for me, but nobody else has used it yet :-D It is still very fresh.

Check it out.

PS: If you ask why not use an existing libraries/solutions, the answer is that I could have, I wanted to, I wanted fully understand how it works, and it was fun.

I have implemented non streamable HTTP MCP from scratch at https://www.ipdefender.eu/mcp. It was reasonably easy. I haven't battle tested it yet, so consider it public alpha.

I also added an OAuth2 implementation written from scratch without any dependencies. It works nicely for me, but nobody else has used it yet :-D It is still very fresh.

I was considering hardening the OAuth2 by adding optional DCR (Dynamic Client Registration), but after a long deliberation I came to the conclusion that DCR does not really bring any additional security to MCP servers where we do not control the amount, type, or anything else about the clients trying to connect to the server. Am I wrong?

I still have one question though. I would like to keep the implementation minimal while locking it down as much as possible. For that reason, I would like to implement a whitelist for OAuth's redirect_uri, but honestly I could not find any exhaustive list of client target URIs.

It appears that most commonly used ones are localhost HTTP addresses, but the hostname can be the application's own ID and the schemes can be arbitrary... depending on OS/platform. I am not sure how to compile such a whitelist, or whether it is even possible/makes sense for open MCP servers where we don't control anything about connecting AI clients.

PS: If you ask why not use an existing solution, the answer is that I could have, I wanted to, and it was fun.

I'm starting from scratch with marketing for my newly rewritten SaaS, which previously only worked in my country, and I am now expanding it to an international audience, so there is currently no global traffic at all.

It is in the trademark monitoring space. The idea is simple: users set it up and get alerts when someone files a similar trademark in a selected territory. It covers 50 countries, and the interface is available in 28 languages, so it can target specific geographic niches in local languages. I am keeping the description general as much as possible to avoid breaking any self promotion rules.

I have set aside an initial budget of $1,000 to test the waters and figure out what works and what does not.

Given the type of service, the international scope, and the relatively small budget, where would you spend this money? How would you split it across channels, and what would your overall approach be?

My original thought was to avoid the usual options like Google Ads and similar platforms, since that is where everyone spends money and competition is high, which drives up prices. Instead, I was thinking about trying more local solutions if they exist.

Any thoughts on this approach? I know most people focus on Google and English only audiences, but I am trying to target multiple regions at once, so I probably need to start with the most cost effective geographic niche first. Am I thinking about this correctly?

Note: A referral or affiliate program is planned for the future, so I need to avoid that for now since I do not currently have the technical infrastructure to manage it.

At the moment I would like to target any of: 🇺🇸, 🇨🇳, 🇯🇵, 🇨🇿, 🇩🇪, 🇰🇷, 🇫🇷, 🇮🇱, 🇵🇱, 🇷🇺, 🇪🇸, 🇮🇳, 🇮🇩, 🇺🇦, 🇮🇹, 🇵🇹, 🇳🇱, 🇸🇪, 🇹🇷, 🇻🇳, 🇹🇭, 🇷🇴, 🇭🇺, 🇩🇰, 🇫🇮, 🇦🇪, 🇳🇴, 🇬🇷

I have a SaaS project that I want to take to the next level. I'm considering starting an affiliate program, but I'm a programmer through and through and have very little experience in that area, so I'm not really sure how to approach it.

I don't want this to come across as self promotion, and without sharing details about the business it may be hard to give useful advice, so I'll try to keep it as generic as possible.

My SaaS provides subscription based monitoring services across multiple countries. The average customer spends around $27/month per country, billed quarterly based on the services they actually use (no upfront annual payments or anything like that).

My initial idea was to offer affiliates 30% of all payments made by a referred customer during their first year.

So my questions are:

• Is 30% too much, too little, or roughly in the right ballpark?
• Is it reasonable that affiliates only get paid when the customer actually pays? For example, the first payment may happen after 30 days, then continue on a quarterly billing cycle.
• Would it be better to offer 50% of the first two payments, 30% of the first four payments, or maybe 100% of the first payment only? I'm looking for a balance between keeping acquisition costs reasonable and making the program appealing enough for partners to actively promote the service.
• Am I thinking about this the right way, or is there a better affiliate structure for a SaaS with this kind of pricing and commission budget?

And a second question:

If I go ahead with this, where do I actually find potential affiliates? Are there specific forums, subreddits, affiliate networks, communities, or other places that work well for SaaS products?

I was surprised to find that I no longer have any free models available at all.

When I purchased Copilot Pro, I was explicitly promised "unlimited code completions and chat interactions with the base model."

So where is my unlimited base model? In my opinion, this looks like a breach of contract. Do I need to enable it somewhere?

https://preview.redd.it/qkhwfdus3f5h1.png?width=629&format=png&auto=webp&s=476a33052a3e9b4b35bf0ce0fa71ee48c131a7b1

I finally shipped it.

Spent the last 2 weeks building a proper translation pipeline for a JS/PHP/HTML app. JS and PHP were easy with standard gettext tooling. HTML was the real nightmare. You know, rich text, links, bolds, nested HTML... And all had to work with AI translator reliably.

HTML translations are not just "replace string A with string B". You need:

• translation context
• surrounding sentences so translators understand intent
• translator comments
• support for wildly different word order
• rich-HTML layouts that survive both tiny and huge translated text

Tons of small things like this one, see how translation for a homepage badge varies from language to language?

<small>Since</small> <b>2015</b>

<small>自</small><b>2015</b><small>年起</small>

<b>2015</b> <small>óta</small>

Same meaning. Different structure.

Then comes the fun part:

• extracting all translatables from mixed HTML/JS/PHP
• feeding dictionaries into AI
• checking that AI did the job right
• compiling translations back safely
• not destroying the UI in the process

Add a language. Run one command. Entire app translated.

Seeing months of internationalization pain collapse into a single command feels slightly unreal.

🇨🇿 🇺🇸 🇨🇳 🇯🇵 🇩🇪 🇰🇷 🇫🇷 🇮🇱 🇵🇱 🇷🇺 🇪🇸 🇮🇳 🇮🇩 🇺🇦 🇮🇹 🇵🇹 🇳🇱 🇸🇪 🇹🇷 🇻🇳 🇹🇭 🇷🇴 🇭🇺 🇩🇰 🇫🇮 🇦🇪 🇳🇴 🇬🇷

TLDR: the price depends on time, probably time of day, and may increase up to 10 times during the day!

I am still not sure what I am paying Ollama for. Last week I ran out of weekly limits while trying to auto translate my app. I was surprised how long a single sentence takes to translate, around 40 seconds. I had to wait until midnight for my weekly limits to reset, and since the translation is fully automated I have logs for every Ollama request. I also log basic stats returned by Ollama like prompt tokens and generated tokens.

So here are the numbers, grab your calculators and infer whatever you want from what I collected during 8 hours of running 3 simultaneous translation threads querying Qwen3.5:

TOTALS for 6.2 percent weekly allowance
req_time 47426.8
req_size 219985
response_tokens 1928844
prompt_tokens 370631

If my calculator is correct I got this for Qwen3.5:

Monthly limit on generated tokens: 133 million (from Monday midnight to 8 am)
Speed: 47 tokens per second

Note that the numbers are approximate since we do not know the exact formula and whether req_time plays a role. It is mainly for orientation.

Limits

Month = 4.29 weeks
Monthly allowance (cumullation of weekly limits): 429%
Qwen3.5 price:

Type	per 6.2% weekly limits	per month limit (429%)
Prompt tokens	370,631	25,645,274
Generated tokens	1,928,844	133,463,560
Request time	13.16 hrs	911 (38 days)

Speed

Phase	Expected Time Share
Tokenization/parsing	< 0.5%
Prefill/prompt processing	10% to 20%
Decode/generation	80% to 90%

Prefill:Decode≈1:6

Total time 47426 - 47426 * 1/7 = 40650 response generation time for 1928844 tokens => 1928844 / 40650 = 47 tokens/s

UPDATE:

It is still running and numbers changed significantly:

Current state:
12.2% weekly limit
req_time 55397.2
response_tokens 2116930
prompt_tokens 414251

New tokens
2,116,930 (now) − 1,928,844 (previous) = 188,086 new generated tokens

12.2 percent (now) − 6.2 percent (before) = 6 percent of weekly limit corresponds to 188,086 new generated tokens

Monthly limit: 188,086 / 6 × 429 = 13,448,149 tokens (previous estimate: 133M)

⚠️ That implies the effective price increased by about 1000% (!!!) over the last 2 hours (around 9 AM UTC).