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@llm-store
#hermesagent
#hermiphication is inevitable 🪽
#opensourceware #osw
Inteligencia artificial en educación inclusiva: estrategias y herramientas para 2026
La inteligencia artificial en educación especial está transformando la forma en que los estudiantes con necesidades específicas acceden al aprendizaje, siempre que se implemente con un enfoque ético, seguro y centrado en el ser humano. En 2026, su adopción es una realidad global, respaldada por herramientas validadas, normativas internacionales y casos de éxito medibles en escuelas de todo el…
Find local businesses, view maps and get driving directions in Google Maps.
UNA IA CON MEMORIA. #GRANDESMODELOSDELENGUAJE #MOLTSCHOOL #OPENCLAW #MOLTBOOK
#moltschool
IA generativa en 2026: Cómo empresas reales desarrollan software en horas
En 2026, la inteligencia artificial generativa es una herramienta clave para el desarrollo rápido de software. Empresas como ServiceNow, Nubank y Mercado Libre ya la usan para automatizar procesos, reducir costos y mejorar la productividad. Aquí te mostramos casos reales, herramientas open source y los pasos para implementarla en tu organización. Continue reading IA generativa en 2026: Cómo…
January 21, 2026 #ZEITGEIST
**Subject:** The post-centralization shift
The explosive, almost surreal ascent of **@opencode** (anomalyco/opencode on GitHub, opencode.ai) captures the exact pulse of early 2026. It is the artifact that defines the **Compute Zeitgeist**.
As of this morning, the repository sits at **~81.5K–82K stars** (with forks exceeding 7.2K–7.3K and monthly active users estimated in the 300K–650K range across community reports and download stats). This isn't incremental traction—it's **escape velocity** from vendor risk. Developers aren't just starring a repo; they're voting with their terminals, migrating to predictable, self-sovereign compute when centralized frontier inference turned brittle under load balancers, rate limits, dynamic quantization, and perceived "nerfs" (especially on heavier tiers like Opus 4.5 during peaks).
This is no longer about chasing the absolute smartest model in isolation. It's about optimizing for **compute sovereignty**: no surprise throttling, no token anxiety mid-crunch, no vendor policy roulette, full control over quantization, context, cost, and privacy. When the proprietary drawbridge rose, OpenCode kept the open one lowered—permanently.
### The Catalyst: The "Anthropic Vacuum"
Claude Code (late 2024) invented the vibe: terminal-native, agentic loop (Plan → Edit → Bash/Git → Verify), LSP-aware codebase context, natural language → multi-file production diffs. It felt like magic—until early 2026, when Anthropic's restrictions (private endpoint blocks on third-party harnesses, peak-hour quality variance, tighter Ultrathink limits) created acute pain for power users.
OpenCode filled the vacuum instantly. It cloned the harness with near-perfect fidelity—preserving muscle memory and zero migration friction—while ripping out the lock-in. Same terminal UX, but now plug-and-play with **+75 models**: Claude when cooperating, OpenAI/Gemini/Groq for bursts, local via Ollama/vLLM/llama.cpp/MLX, or routed via Zen/OpenRouter. The default reflex became: "Anthropic hiccup? Switch to local GLM in OpenCode." Continuity, not crisis.
### The Trigger Timing: GLM-4.7 & MiniMax M2.1 as True Agentic Alternatives
The star surge accelerated dramatically after **December 22, 2025** (GLM-4.7 from Zhipu AI / Z.ai) and **December 22–23, 2025** (MiniMax M2.1). These were the first open-weight (or near-open accessible) frontier models tuned explicitly for agentic coding stability:
- **GLM-4.7** — 200K+ context, 55+ t/s inference, dedicated "Preserved Thinking" / "Deep Thinking" modes for coherent multi-step reasoning across long sessions. It maintains architectural coherence where nerfed remote Opus falters under load, often matching or beating Claude 4.5 Sonnet on coding benchmarks (e.g., LiveCodeBench ~84.8%, strong on τ²-Bench agentic interaction). Local vLLM or SiliconFlow routing makes it the reliable daily driver for complex, iterative work.
- **MiniMax M2.1** — Tuned for "vibe coding" (native Android/iOS generation, multi-language like Rust/Go), massive 1M-token context potential, low-latency efficiency. It punches above its ~10B active params in execution-heavy flows, especially frontend/mobile stacks.
Paired with OpenCode's model-agnostic backend (client/server split, TUI as mere frontend), these created the "Big Pickle" setups: GLM for deep reasoning/architecture, MiniMax for vibe/implementation speed. No more single-provider polish—**predictable sovereignty** won.
### Resiliency Routing & The Ecosystem Supernova
Community norms evolved to **antifragility**:
- Auto-failover in oh-my-opencode: If Anthropic stutters, route to local GLM-4.7-Flash or Zen-routed MiniMax. Zero downtime.
- **oh-my-opencode** — Far beyond config; it's a full workflow overhaul. The star feature: **Sisyphus** agent (batteries-included orchestrator using Opus 4.5 High or equivalents). It delegates parallel sub-agents (oracle, librarian, frontend engineer), enforces TODO-driven loops, runs background tasks aggressively, and automates Plan → Build → Verify with frightening efficiency—commoditizing small-to-medium engineering roles.
- **Clavix** — Matures structured prompting into a CLI discipline: agentic-first templates ensure max model adherence before inference.
- **Openwork** (Composio + OpenCode) — Chains local files/terminal to cloud apps (Slack, Jira, GitHub) in pure OSS flows.
- Guardrails like **CC Safety Net** for enterprises, Antigravity OAuth hacks for "free-ish" premium access, Ralph-TUI integrations, Skills.sh ecosystem (npx add Remotion/browser agents).
Viral fuel keeps pouring in: YouTube "OpenCode + Oh My OpenCode (Sisyphus)" demos, Reddit/LinkedIn "twice as good" threads, X spikes on every Anthropic stumble, even enterprise whispers (Copilot SDK previews + plugins). Recent CVEs and fixes show the project moving at real-world velocity.
### The New Truth
OpenCode embodies 2026's core shift: The moat isn't model weights anymore—it's the **harness + ecosystem + local fallback**. Your setup (Claude Code for rare Ultrathink highs, OpenCode/local GLM for the daily grind) isn't fringe—it's the converging archetype. We've moved from renting intelligence to owning the machinery that wields it.
The atmosphere? Electric, decentralized, resilient, accelerating. This isn't a tool win—it's sovereignty reclaimed.
Los Grandes Modelos de Lenguaje y la matemática necesaria para su construcción
⸻
QUICKCOURSE · 7m
Los Grandes Modelos de Lenguaje
La matemática necesaria para su construcción
🏷️ #GrandesModelosDeLenguaje
⸻
Los grandes modelos de lenguaje no son magia.
No aparecen de la nada.
No “piensan” por sí solos.
Están construidos sobre matemáticas muy concretas.
No todas las matemáticas.
No exceso académico.
Solo las que explican qué están haciendo los modelos
y por qué funcionan.
Este recorrido es un mapa.
No para memorizar fórmulas,
sino para entender la estructura que sostiene a los LLMs.
⸻
1. Estadística y Probabilidad
El lenguaje de la incertidumbre
Los modelos de lenguaje aprenden de datos reales.
Y los datos reales son ruidosos, incompletos e imperfectos.
La estadística existe para responder una pregunta fundamental:
¿Qué podemos concluir cuando no lo sabemos todo?
Eso es aprender.
⸻
Poblaciones y muestras
En teoría existe una población:
todo lo que podría decirse, escribirse o leerse.
En la práctica, solo vemos muestras.
Los modelos no fallan porque “no son inteligentes”.
Fallan porque aprenden de muestras mal tomadas.
El sesgo, la varianza y la representatividad
deciden si un modelo generaliza
o solo memoriza.
⸻
Promedios, expectativas y variación
El promedio no es solo un número.
Es una idea.
El valor esperado es el corazón de los objetivos de entrenamiento.
Cuando un modelo optimiza,
está persiguiendo una expectativa.
La varianza mide incertidumbre.
La covarianza mide relaciones.
Sin entender esto,
no se entiende el sobreajuste
ni la inestabilidad de los modelos.
⸻
Distribuciones y supuestos
Los datos no son neutrales.
Siempre asumimos algo sobre cómo se generan.
Distribuciones como la gaussiana, la binomial o la uniforme
no describen la verdad absoluta.
Describen supuestos operativos.
Los modelos funcionan
porque esos supuestos suelen ser útiles.
⸻
Bayes y aprendizaje
Aprender es actualizar creencias.
La probabilidad condicional y el teorema de Bayes
formalizan cómo incorporar nueva información.
Los LLMs modernos dependen profundamente
de este tipo de razonamiento probabilístico.
⸻
2. Álgebra Lineal
La estructura del lenguaje computable
Si la estadística explica la incertidumbre,
el álgebra lineal explica la forma.
En un modelo de lenguaje, todo es geometría.
Palabras son vectores.
Textos son matrices.
Modelos son transformaciones.
⸻
Vectores, matrices y tensores
Los embeddings no son palabras.
Son puntos en espacios de alta dimensión.
Las capas no “razonan”.
Transforman vectores una y otra vez.
Los tensores permiten que esto ocurra
a la escala necesaria para el lenguaje humano.
Cuando entiendes esto,
las arquitecturas dejan de parecer misteriosas.
⸻
Transformaciones y descomposición
Cada capa aplica una transformación lineal.
Una y otra vez.
Conceptos como rango, eigenvalores y descomposiciones
explican estabilidad, colapso y eficiencia.
Métodos como SVD o PCA
permiten reducir ruido y complejidad.
Sin álgebra lineal,
los modelos simplemente no escalan.
⸻
3. Cálculo
Aprender es optimizar
Entrenar un modelo no es comprender.
Es optimizar.
El cálculo explica cómo un modelo se mueve
en un espacio inmenso de parámetros.
⸻
Gradientes y ajuste
Las derivadas miden sensibilidad.
Los gradientes indican dirección.
El descenso por gradiente
no es inteligente.
Es persistente.
Pero funciona.
Backpropagation no es magia.
Es la regla de la cadena aplicada a gran escala.
⸻
Paisajes de optimización
Los modelos no entrenan en superficies simples.
Existen mínimos locales,
puntos silla,
regiones planas y regiones inestables.
Entrenar un LLM es navegar un paisaje complejo,
no encontrar una solución perfecta.
⸻
Cómo aprender esta matemática hoy
No se empieza con libros densos.
Se empieza con intuición.
Visualizar primero.
Entender la geometría.
Sentir el cambio.
Luego, estructura formal.
Cursos aplicados.
Práctica constante.
Finalmente, conectar teoría con modelos reales.
Ahí es donde la matemática se vuelve operativa.
⸻
Cierre
Los Grandes Modelos de Lenguaje
no son misteriosos.
Están construidos con:
• probabilidad para manejar incertidumbre
• álgebra lineal para estructurar información
• cálculo para optimizar comportamiento
Aprender esta matemática
no es un filtro elitista.
Es aprender la gramática profunda
de la infraestructura cognitiva de nuestra era.
Y entender la gramática
es entender el poder.
🏷️ #GrandesModelosDeLenguaje
The Agentic Coding Revolution: How Claude Code and Google Antigravity Are Democratizing Software Development
The Line Was Drawn. The World Followed.
When Anthropic shipped Claude Code in late 2024, they weren’t just releasing another coding assistant. They built an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows through natural language commands. It was a declaration: the future of coding is agentic, not autocomplete.
Google heard that declaration loud and clear.
On November 18, 2025, Google launched Antigravity, describing it as an agentic development platform that lets agents autonomously plan, execute, and verify complex tasks across your editor, terminal, and browser. The message was unmistakable: the agent-first era had officially arrived, and the race was on.
But this isn’t just about two tech giants one-upping each other. This is about what happens when the barrier between human intention and working software collapses entirely.
## The Shift From Typing to Thinking
Traditional coding has always been a translation problem. You think in concepts—user authentication, real-time updates, payment processing—but you type in semicolons, brackets, and variable declarations. The cognitive overhead is enormous. The friction is constant.
Claude Code is intentionally low-level and unopinionated, providing close to raw model access without forcing specific workflows, creating a flexible, customizable, scriptable, and safe power tool. Instead of suggesting what you might want to type next, Claude Code executes entire workflows. You describe what you want. The agent figures out how to build it.
Google Antigravity took this philosophy and scaled it. The idea is that AI shouldn’t just suggest code; it should be an autonomous partner that can navigate the terminal, the editor, and even a live browser to get things done. Antigravity completes a typical Next.js with Supabase feature in 42 seconds versus Cursor’s 68 seconds, and on codebase navigation across 100,000-plus line repositories, Antigravity resolves queries 40% faster than Cursor.
These aren’t incremental improvements. They’re structural ones.
## The Multi-Agent Paradigm
The real breakthrough isn’t individual agents getting smarter. It’s the emergence of multi-agent orchestration as a first-class workflow.
At Anthropic, Claude Code has become far more than a coding tool—it has begun to power almost all of their major agent loops, used for deep research, video creation, and note-taking, among countless other non-coding applications. Antigravity treats learning as a core primitive, allowing agents to save useful context and code snippets to a knowledge base to improve future tasks.
Think about what this means: developers are no longer managing files and functions. They’re managing intelligent systems that learn from their work, collaborate with each other, and compound their effectiveness over time.
Antigravity’s agent-first Manager surface flips the traditional IDE paradigm—instead of the agent being embedded in the interface, the interface is embedded into the agent. You’re not coding anymore. You’re conducting.
## Four Billion New Developers
Here’s where it gets exponential.
Language models didn’t just make programmers faster. They fundamentally changed who can be a programmer.
For decades, software development required fluency in arcane syntax, intimate knowledge of data structures, and years of experience debugging cryptic error messages. The barrier to entry was intellectual, yes, but also linguistic—you needed to speak the machine’s language.
LLMs inverted that relationship. Now the machine speaks yours.
Natural language allows systems to do tasks that require human-like commonsense knowledge, and when implemented with existing LLM platforms such as GPT-4, AI actions saw a big increase in task accuracy from 59 to 89 percent. Recent LLMs like OpenAI’s GPT-4 and Codex, Meta’s Code Llama, and DeepMind’s AlphaCode can interpret informal problem descriptions and produce functional code with minimal human intervention, altering the role of the programmer and the development workflow.
Every person who can clearly articulate a problem can now build software to solve it. That’s not four billion “no-code” users clicking through drag-and-drop interfaces. That’s four billion people with direct access to computation itself.
The product manager who sketches a workflow can now ship it. The researcher with a data analysis need can build the tool. The small business owner with a specific operational problem can automate it—without hiring a development team or learning JavaScript.
This isn’t theoretical. It’s happening right now.
## The Open Source Tsunami
The open-source community didn’t wait for permission. They saw what Claude Code could do and immediately started building.
OpenCode emerged as an open-source alternative that could connect to multiple model providers—Claude, GPT, Gemini—giving developers model flexibility without vendor lock-in. When Google launched Antigravity with generous free quotas on Gemini 3 Pro, the community responded with plugins, extensions, and integrations at breakneck speed.
A community member built an open-source extension called Toolkit for Antigravity that adds real-time quota tracking for hour-by-hour stats for Gemini, Claude 4.5, and OpenAI GPT models, with cache management and privacy-first local operation with no telemetry.
This is the pattern that defines technological revolutions: the tools get democratized, the community starts iterating, and suddenly progress becomes nonlinear.
## What Developers Actually Do Now
Anthropic’s Data Infrastructure teams let Claude Code use OCR to read error screenshots, diagnose Kubernetes IP exhaustion, and provide fix commands, while non-technical finance staff can simply describe requirements in natural language and Claude Code automatically generates queries and outputs Excel reports.
Product Development teams in auto-accept mode let Claude Code autonomously write 70% of Vim mode code, Security Engineering uses Claude Code to quickly parse Terraform plans and complete security reviews, and Inference teams rely on Claude Code to generate unit tests covering edge cases, reducing research and development time by 80%.
The developer role is evolving from implementer to architect, from typist to conductor, from code writer to system designer.
This is Anthropic’s favorite workflow for changes that are easily verifiable with tests: ask Claude to write tests based on expected input/output pairs, then test-driven development becomes even more powerful with agentic coding.
## The Inevitable Acceleration
Claude Code showed the line. Google Antigravity proved the market. The open-source community is now multiplying the possibilities.
Google acqui-hired Windsurf’s leadership team for $2.4 billion, extracted talent and IP, then shipped Antigravity four months later—these are people who’ve been thinking about autonomous coding for years, now moved inside Google’s infrastructure with unlimited resources.
The pace isn’t slowing down. It’s accelerating.
Every major tech company is now racing toward the same inevitable conclusion: the future of software development is conversational, agentic, and accessible to anyone who can describe what they want to build.
## The New Software Economy
When four billion people can build software, what happens to the software industry?
The answer isn’t displacement. It’s expansion.
Just as spreadsheets didn’t eliminate accountants—they made financial modeling accessible to everyone and created entirely new categories of business intelligence—agentic coding won’t eliminate developers. It will explode the addressable market for software itself.
Every small problem that was too expensive to solve with custom software becomes solvable. Every workflow that was “close enough” with existing tools can now be perfect. Every business process that required manual work can be automated by the person who understands it best.
The bottleneck was never ideas. It was implementation. That bottleneck just disappeared.
## What Comes Next
We’re watching the formation of a new development paradigm in real time.
Antigravity is designed as an agent-first platform that presupposes that the AI is not just a tool for writing code but an autonomous actor capable of planning, executing, validating, and iterating on complex engineering tasks with minimal human intervention.
This is what the next decade of software looks like: developers managing fleets of specialized agents, each learning from the work of others, building on shared knowledge bases, and collaborating to solve problems that would have required entire teams.
The question isn’t whether this future arrives. It’s how quickly you adapt to it.
Because the line has been drawn. Claude Code drew it. Google Antigravity reinforced it. The open-source community is now making it inevitable.
And four billion new developers are about to change everything.
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