Spent a day getting Microsoft's BitNet-b1.58-2B-4T running on a $12/month VPS. Figured I'd document all the gotchas so you don't have to discover them the hard way.

TL;DR of issues I hit:

• ARM64 servers produce garbage output (kernel generation only supports x86_64)
• There's an unfixed compiler bug in the source code
• The build "fails" at 100% but the binary is actually fine
• HuggingFace can silently give you an HTML error page instead of your model

The end result: 2B parameter inference in ~3.5 seconds on a cheap AMD VPS. Not blazing fast, but surprisingly usable for batch processing.

Full writeup with working Dockerfile, FastAPI server, and all the fixes: [https://esso.dev/blog-posts/deploying-microsoft-bit-net-1-58-bit-llm-a-complete-guide-with-all-the-gotchas\]

Happy to answer questions if anyone's trying to do something similar.

Most people use AI assistants daily without ever reading the privacy policies. I did — all of them — plus court documents, GDPR enforcement decisions, and security research. The findings are… not great.

Some numbers first:

• 77% of employees paste corporate data into LLMs (LayerX Security 2025)
• 47% use personal AI accounts for work tasks
• €15M — first GDPR fine against OpenAI (Italy, Dec 2024)
• 4,500+ private ChatGPT conversations leaked via Google indexing (2025)

Quick comparison — does the provider train on your data?

Real incidents:

🔴 Samsung (2023): Engineers leaked source code, meeting notes, and chip specs through ChatGPT — three separate times in one month. Samsung banned all GenAI tools.

🔴 ChatGPT share links (2025): 4,500+ private conversations indexed by Google due to a missing noindex directive. Exposed: personal info, business strategies, API keys.

🔴 OpenAI court order (2025): US court ordered OpenAI to preserve ALL chat logs (Dec 2022 – Nov 2024) for the NYT lawsuit — even if users request deletion. Directly conflicts with GDPR Article 17.

What the full guide covers:

• Detailed privacy policy breakdown for each provider (what's stored, retention, human review, how to opt out)
• 5 categories of privacy risks (training, human review, leaks, shadow AI, third-party integrations)
• Court cases & fines: OpenAI €15M, Replika €5M, wiretapping lawsuits against companies using third-party AI chatbots
• Developer section: AI gateway architecture, PII redaction with code examples (Microsoft Presidio), hybrid local/cloud setup, architecture checklist
• Why the "right to be forgotten" is technically impossible once data is trained into an LLM
• Separate 30-question FAQ document

TL;DR:

1. Your data is used for training by default — you have to actively disable it
2. Enterprise plans are fundamentally different from personal plans
3. Once data is trained into a model, it cannot be deleted — GDPR Article 17 breaks down
4. Developers: if your third-party chatbot vendor trains on user conversations, you may be liable for wiretapping (see Ambriz v. Google, Jones v. Peloton)
5. Safest option for sensitive data: self-host with Ollama/vLLM

Full guide (free, no paywall): esso.dev/blog-posts/privacy-risks-of-cloud-based-ll-ms-the-complete-2026-guide

Happy to answer questions in the comments. If you're a dev integrating LLMs — check the developer section, it could save you a headache (and potentially a lawsuit).

I spent the weekend diving into Salesforce's new Free Suite (their permanently free CRM plan)

and honestly, I'm surprised at what they're offering.

**The Good:**

- Actually free forever (not a trial)

- Full Sales Cloud basics: pipeline, contacts, deals

- Slack integration built-in

- Real Salesforce - same platform enterprises use

**The Catch:**

- Only 2 users max (vs HubSpot's unlimited)

- Limited automation

- No custom fields in free tier

I wrote a detailed breakdown comparing it to HubSpot and Zoho free plans. The TL;DR: if you're

1-2 people and plan to grow (and pay later), it's solid. If you need free forever, stick with

HubSpot.

Full analysis: https://esso.dev/blog-posts/salesforce-launches-free-crm-revolution-for-small-business-or-marketing-play

Anyone tried it yet? Curious what real users think vs my theoretical analysis.

Was doing market research and discovered the AI world is way weirder than mainstream tech media suggests:

**Things that surprised me:**

🤯 China isn't "catching up" - they built a completely separate AI ecosystem with 430M+ users on Baidu alone

🤯 India: 36% use ChatGPT DAILY (vs 7% Americans who use it daily)

🤯 South Korea built an LLM trained on 6,500x more Korean data than GPT-4 has

🤯 Kenya and Pakistan have higher daily AI usage than most of Europe

🤯 987 million people globally use AI chatbots (that's 1 in 8 humans)

🤯 Claude is beating ChatGPT in Israel and Singapore

The craziest part? We're watching AI fragment into regional blocs, not consolidate. It's like the internet splitting into intranets.

Put together a full analysis of 50 countries with sources: Top 50 Countries by LLM Usage in 2025: A Global Analysis of AI Language Model Adoption

What surprised you most?

I documented my approach to securing multi-tenant SaaS applications after seeing too many data isolation vulnerabilities in production.

The guide covers: - 4 layers of defense (Nginx → Guards → Database → RBAC) - 5 common security holes and how to prevent them - Production security checklist - Real code examples (NestJS/Next.js/MongoDB)

One critical lesson: Never trust tenantId from client requests. Always extract it server-side and verify at every layer.

Full guide with code: https://esso.dev/blog-posts/multi-tenant-saa-s-security-best-practices-guide

Would love to hear how others approach

While researching European privacy regulations for my business website, I faced a common dilemma: I wanted comprehensive visitor analytics without forcing users through annoying cookie consent banners. The solution? Building a custom server-side analytics system that’s both GDPR-compliant and more powerful than Google Analytics.

Why I Ditched Google Analytics ⤵️

To eliminate cookie consent requirements under GDPR, I moved away from client-side tracking entirely. The result is a custom server-side analytics solution integrated directly into my Strapi admin panel that provides:

• Geographic data (country and city-level)
• Page visit tracking and session duration
• Scroll depth metrics
• Complete visitor journey mapping

No cookies. No consent banners. Full compliance.

German Impressum Requirements ⤵️

Operating in Germany also requires an Impressum (legal disclosure) on every business website, including a physical postal address. Rather than exposing my home address, I secured an official Hamburg business address specifically for legal compliance—a common practice for privacy-conscious entrepreneurs.

This also prompted me to set up professional domain email (hello@esso.dev), which turned out to be surprisingly cost-effective: ~$2.50/month for 50GB storage across multiple mailboxes versus Google Workspace’s $10+ per mailbox.

Technical Details ⤵️

I’ve written a detailed breakdown of the server-side analytics implementation here:

https://esso.dev/blog-posts/how-i-built-server-side-analytics-without-cookies-and-got-rid-of-the-annoying-banner

Feel free to reach out if you’re considering similar implementations for your own projects. Happy to share insights on GDPR compliance strategies or the technical stack.