Published judgement, not opinion
Technical analysis of models, local AI, image, video and voice, agents, cybersecurity and regulation. Written by the team that ships them to production, with primary sources and a review date.
Every analysis, by topic
Filter by topic. Every article shows its date, its author and its sources.
11 analyses
Chatbot vs AI agent: which one your business needs
A chatbot answers, an agent acts. Definitions from Anthropic and OpenAI, a decision table, agent washing, costs, risks and when a plain workflow or form is the better buy.
Strategy and regulationHow to build a great event networking app
Lessons from the apps we built for Colombia Tech Week 2026, Colombia Tech Fest and Réseau Mazette: access, agenda, directory, meetings, offline mode and AI.
ModelsClaude Opus 5.5: what changes for businesses
Launched September 22, 2026: cheaper than Opus 5, Fable 5.1-level results per Anthropic. Pricing, benchmarks, breaking changes and when to use it.
ModelsThe AI model map, October 2026
Claude Opus 5.5 and Sonnet 5.5, GPT-6 Astra, Gemini 4 Argon, Grok 4.7, Muse Spark: who leads in October 2026, what each price tier costs and what to test.
Local AI and open weightsLocal AI in 2026: which hardware to buy and what runs on it
RTX 5090, RTX PRO 6000, DGX Spark, Ryzen AI Max+ and Mac Studio M5: memory math, what fits where, buy-vs-rent costs and picks by team size.
Local AI and open weightsUncensored models: what they are and what they risk
What removing a model's safeguards really means, why the ecosystem exists, what it breaks, what the law bans in 2026 and what to use instead.
CybersecurityDeepfake fraud: a protocol for companies
From Arup's $25.6M loss to FBI figures: how cloned-voice and deepfake fraud works, and a verification protocol for finance, HR and executive assistants.
Local AI and open weightsOpen-weight models in 2026: which to use, under which license
Kimi K3, Qwen3.8, DeepSeek V4.1, GLM-5.3, Gemma 4, gpt-oss, Mistral: sizes, licenses, gap to the frontier and how to host them without legal surprises.
Strategy and regulationThe real cost of AI
How AI is billed now, why cost per solved task beats price per token, savings levers with figures, hidden costs and a worked local vs API break-even.
Agents and engineeringFine-tuning vs RAG: the criterion
When RAG, when fine-tuning and when a hybrid: the decision order before paying for any training.
CybersecurityWhat to test in a web and API pentest
Not a scanner PDF: web apps and APIs, manual validation of every finding and a retest, priced on the outcome.
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How we write every analysis
The same rules we apply on Slash AI Lab projects.
Primary sources
Official announcements, technical documentation, papers and legal texts, linked at the end of every article.
Date and version
Models change every month. Each analysis states the date and the versions it covers.
No invented numbers
If we can't verify a figure, we don't publish it. Our opinions are presented as opinions.
Production judgement
We write what we would apply on a real project: costs, risks and limits stated up front.
The questions we hear often
Who writes these analyses?
The founding team of Slash Digital Lab (Jean Martial, Rémy Sanchez and Esteban Zuleta), drawing on the work of Slash AI Lab. Every article carries a byline, a date and sources.
How often are they updated?
We review the articles on models and pricing every quarter, or sooner when a release changes the recommendation. Each article shows its review date.
How do you pick topics?
They are the questions clients ask us on real projects: which model to use, when local AI makes sense, how to secure an agent, what regulation requires.
Can I quote or reuse the content?
Yes, with a link back to the article. Third-party data belongs to its authors and is linked in every analysis.
Do you run custom evaluations?
Yes: Slash AI Lab runs the same method on your prompts, your data and your cost and privacy constraints.
From analysis to project
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