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.
Four reading paths, by role
The analyses each team asks us for most, in the order worth reading them.
CTOs, data and engineering
- The AI model map, October 2026
- Chatbot vs AI agent: which one your business needs
- RAG in 2026: long context, hybrid search and agentic search
- Evals: how to test an LLM system
CISOs and risk teams
- Prompt injection and agent security: risk number one
- Deepfake fraud: a protocol for companies
- AI supply chain security: models, packages and MCP servers
CMOs, brand and experience
- How to build a great event networking app
- Image models in 2026: which to use for brand work
- Video models in 2026: from demo to ad
- Rights and provenance of AI content
Every analysis, by topic
Filter by topic. Every article shows its date, its author and its sources.
32 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.
CybersecurityPrompt injection and agent security: risk number one
Why prompt injection is still unsolved, what the 2025–2026 incidents show and which controls actually work for agents and chatbots.
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.
Image, video and audioImage models in 2026: which to use for brand work
Nano Banana 2.1 and Pro, GPT Image 2.5, Qwen-Image: which model to use for brand assets, what each image costs and what the AI Act requires since August 2026.
Agents and engineeringAI agents in production: what works in 2026
Workflow or agent, the eight building blocks, adoption and failure data, computer use, design rules, costs, security and a launch checklist.
Strategy and regulationThe 2026 AI regulatory map
The AI Act after the Omnibus, GDPR, Colombia's Law 1581 and SIC rules, LatAm, the US and ISO 42001: dates, duties and a 10-step plan for companies.
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.
Image, video and audioVideo models in 2026: from demo to ad
Gemini Omni Flash, Veo 3.1, Kling 3.0, Seedance, Wan 3.0: what works for ads, what a second costs, what the Sora shutdown teaches and how to label in the EU.
Agents and engineeringMCP explained for companies
What the Model Context Protocol is, how it evolved to the 2026-07-28 spec, who governs it, how it differs from A2A and how to adopt it safely.
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.
ModelsReasoning models: when paying for thinking pays off
Thinking costs tokens and seconds. How Anthropic, OpenAI and Google expose effort, where it pays, where it doesn't, and how to tune it by cost per solved task.
Agents and engineeringAgentic coding: how a software team changes in 2026
Claude Code, Codex, Copilot and Cursor now open pull requests on their own. What the benchmarks and studies really show, and how specs, tests and review must change.
CybersecurityHow AI changes pentesting
What attackers now do with AI, what Mythos, Big Sleep and XBOW find, why validation became the scarce skill and how to adapt your pentest in Colombia.
Image, video and audioVoice AI in 2026: TTS, transcription, voice agents and music
GPT-Live-1, Gemini 3.8 Live, TTS and transcription for Spanish and French, Suno v6 and the music lawsuits, voice cloning, and how to build a compliant voice agent.
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.
Local AI and open weightsQuantization without myths: GGUF, AWQ, FP8 and FP4
What 4, 5 or 8 bits really cost in quality, memory and speed, which format runs where (GGUF, AWQ, EXL3, FP8, NVFP4) and how to validate a quant.
Agents and engineeringRAG in 2026: long context, hybrid search and agentic search
A million-token window does not retire retrieval. What works in 2026: contextual chunks, hybrid search, rerankers, agentic search, access control and a cost model.
CybersecurityGuardrails that work in production
The seven layers of a guardrail stack, the 2026 guard models compared, how to measure them in your languages and two reference architectures.
Image, video and audioRights and provenance of AI content
Who owns AI output? Lawsuits and settlements, vendor indemnities, likeness and voice, AI Act Article 50, C2PA, SynthID and a practical policy for brand teams.
Local AI and open weightsFrom laptop to server: Ollama, llama.cpp, vLLM and SGLang
Which engine for one person, a team or production, the techniques that multiply throughput, how to secure and monitor it, and when an API is cheaper.
Agents and engineeringEvals: how to test an LLM system
Why eyeballing fails: a golden set in Spanish and French, calibrated LLM judges, CI regression gates, tooling and what running evals costs.
CybersecurityAI supply chain security: models, packages and MCP servers
Models that run code, hallucinated packages, malicious MCP servers and skills: 2025–2026 incidents, controls that work and the EU CRA reporting duties.
Image, video and audioGenerative AI in live brand experiences and DOOH: what works
AI photobooths, face swaps and DOOH screens: latency, moderation, biometric consent, AI Act labelling and how to measure a live activation.
Local AI and open weightsSmall models and on-device AI: when they are enough
Apple, Google and Microsoft now ship small models inside the OS. What they do well, what they can't, and how to choose between device, server and cloud.
ModelsHow we choose a model
Language, code, agents, cost and privacy: the evaluation we run before every deployment, with 30 real prompts per client.
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.
CybersecurityPentest in Colombia: what to really test
Not a scanner PDF: web apps and APIs, manual validation of every finding and a retest, priced on the outcome.
Strategy and regulationDo AI engines cite you?
30 fixed prompts, 5 engines and 3 languages to measure whether ChatGPT, Claude or Perplexity cite a company correctly. No invented numbers.
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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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