# Insights: AI, models and cybersecurity analysis Source: https://slash-digital.io/en/insights/ Insights · Slash AI Lab 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. - 19 published analyses - 6 topics - ES · EN · FR same content Start a project Browse the library Featured · Agents and engineering ## Grok Bot with Claude Opus 5.5: our preferred agent Our position on October 8, 2026: Grok Bot over Muse and Dots, for model routing to Claude Opus 5.5, team features and availability. Criteria, comparison and caveats. Jean MartialOctober 8, 20268 min Read the analysis → Where to start ## Four reading paths, by role The analyses each team asks us for most, in the order worth reading them. Technology CTOs, data and engineering - The AI model map, October 2026 - Chatbot vs AI agent: which one your business needs Slash AI Lab → Security CISOs and risk teams - Prompt injection and agent security: risk number one - Deepfake fraud: a protocol for companies Pentest Colombia → Marketing and brand CMOs, brand and experience - How to build a great event networking app - Video models in 2026: from demo to ad Interactive experiences → Library ## Every analysis, by topic Filter by topic. Every article shows its date, its author and its sources. Search the analyses All 18 Models 3 Local AI and open weights 4 Image, video and audio 1 Agents and engineering 4 Cybersecurity 4 Strategy and regulation 2 18 analyses Agents and engineering ### 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. Oct 12, 20268 minRémy Sanchez Strategy and regulation ### How 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. Oct 8, 20267 minJean Martial Models ### Claude 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. Updated Oct 28, 202611 minJean Martial Models ### The 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. Updated Oct 28, 202610 minJean Martial Cybersecurity ### Prompt 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. Updated Nov 1, 20269 minRémy Sanchez Local AI and open weights ### Local 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. Sep 26, 20268 minRémy Sanchez Agents and engineering ### AI 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. Updated Nov 2, 20269 minRémy Sanchez Local AI and open weights ### Uncensored 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. Updated Oct 28, 202616 minRémy Sanchez Image, video and audio ### Video 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. Updated Nov 3, 20269 minJean Martial Cybersecurity ### Deepfake 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. Sep 26, 20268 minEsteban Zuleta Local AI and open weights ### Open-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. Updated Oct 28, 20269 minRémy Sanchez Models ### Reasoning 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. Updated Oct 30, 20269 minRémy Sanchez Agents and engineering ### Agentic 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. Updated Oct 31, 20269 minRémy Sanchez Strategy and regulation ### The 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. Updated Oct 28, 20269 minEsteban Zuleta Local AI and open weights ### Quantization 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. Updated Oct 30, 20268 minRémy Sanchez Cybersecurity ### Guardrails 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. Updated Oct 30, 20269 minRémy Sanchez Agents and engineering ### Fine-tuning vs RAG: the criterion When RAG, when fine-tuning and when a hybrid: the decision order before paying for any training. Updated Sep 26, 20263 minJean Martial Cybersecurity ### What 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. Updated Oct 14, 20263 minEsteban Zuleta No analysis matches your search. Try another word or choose "All". Show more analyses (0) Editorial method ## 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. Frequently asked questions ## 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.