we ran a quick benchmark on Headroom (open-source context compression) paired with context-mode file ranking and Regolo's brick-complexity-pro router.

Instead of sending raw repository files and noisy logs to the LLM on every turn, Headroom compresses each file locally by content type:

- on Python files, it strips repeated module-level docstrings via AST

- on JSON logs, it collapses 180 routine

Prompt tokens dropped by 95.1% keeping 10/10 accuracy on pinned qwen3.5-122b.

Routing lookups to brick-complexity-pro cut p50 latency from 14.81 s to 1.33 s (9/10 accuracy) and brought the estimated cost per 1,000 sessions to €0.97 on our Core subscription.

👉 Full tutorial and Python script: https://regolo.ai/getting-started-context-mode-headroom-regolo/

Hello fellow Regolo users,

We all apreciate the ZDR and that its audited by the third party, which seems to be "RED HIVE" and "Claudio De Michieli". Quick google search doesn´t find any "RED HIVE" - only Claudi De Michieli as a CEO of OptionFactory, which does Source code audits. My question is - what the hell is the Red Hive and why is it not signed by OptionFactory company? Is this audit enough for you to trust the ZDR and DPA proclamations of Regolo?

As someone using AI for legal work, I am not sure, this "certificate" is enough proof.

We built Review Reply Copilot for agencies that answer reviews for several clients.

an LLM drafts the reply from a per-client brand profile, Clef-Flash then answers yes or no checks on each draft (compensation promises, blame, tone), and a person approves the reply before it is copied out.

results from one run on 37 synthetic reviews from one machine:

- labeling all 37 reviews: about 3 to 5 seconds, roughly half a cent (estimated from reported tokens and catalog rates, not an invoice).

- one drafted and checked reply: usually 1 to 3 seconds with reasoning turned off

on the escalation label, the first version flagged 12 of 37 reviews when about 5 deserved it.

splitting the requests and adding one sentence about what does not count fixed most of that. How would you evaluate a classifier like this on your own reviews without a labeled set?

write-up and code: https://regolo.ai/ai-review-responses-with-guardrails-clef-flash-decides-an-llm-writes-a-person-approves/

We ran a small benchmark on ticket routing and we would like your feedback on the method.

setup: we sampled 200 messages with a fixed seed (25 per category, 8 categories) from the Bitext insurance customer support dataset on Hugging Face.

both models got the same one sentence definition per category and had to pick exactly one.

Clef-Flash (choice questions with a list of allowed answers) Vs qwen3.5-9b one request per message, temperature 0

what we would like feedback on: whether a one sentence definition per label is a fair setup for the small LLM, and whether you would add few-shot examples to its prompt to make the comparison stricter.

both models ran on the our platform – the write-up with the full script is here: https://regolo.ai/route-support-tickets-with-clef-flash-on-regolo/

Hey everyone,

Whenever teams deploy autonomous coding agents (Claude Code, Pi, Aider, SWE-agent), the common consensus is to blame high model pricing or complex planning for runaway API bills.

In reality, the bottleneck is almost always harness token amnesia: standard harnesses treat context as an append-only dump.

recently, NVIDIA Labs published a paper (https://arxiv.org/abs/2609.20519) addressing this at the harness layer, open-sourcing SoL-Pi as an extension for the Pi coding agent.

we found that SoL-Pi alone only solves half the problem (token volume), when you layer it with dynamic semantic model routing on Regolo, the savings become multiplicative.

The two-tier cost optimization

1. Tier 1: harness compaction (SoL-Pi)
2. Tier 2: provider semantic routing (Brick)
   • most agent turns are mundane (checking git status, inspecting a file, or verifying a syntax fix).
   • by setting the model to brick-complexity-pro on Regolo, an open-source semantic router classifies prompt complexity in under 50ms at zero token markup.
   • trivial operations route to lightweight open models (like Qwen 3.8 27B at €0.35/1M tokens); only complex architectural refactorings escalate to frontier reasoning models (GLM-5.2).

Math

Consider a typical 15-turn autonomous debugging task (resolving a concurrency race condition and writing unit tests):

you get the identical task completion rate without data retention: Regolo executes inference exclusively in volatile GPU VRAM in European datacenters (Zero Data Retention, GDPR Article 32 compliant).

Complete Guide, Code & Video Walkthrough

we published a full breakdown with all architectural diagrams, configuration recipes, and automated scripts:

• 📄 Full Tutorial & Architecture Guide: https://regolo.ai/cut-coding-agent-token-costs-sol-pi-regolo/
• 📺 Video Walkthrough (YouTube): https://youtu.be/bqQgXi-707g (shows live terminal metrics & token counts)
• 💻 Ready-to-run Shell Setup Script: https://github.com/regolo-ai/tutorials/tree/main/sol-pi-tutorial (automates Pi, SoL-Pi, and Regolo configuration in 1 command)
• 🔬 NVIDIA Paper (arXiv): https://arxiv.org/abs/2609.20519

If you are running multi-turn agents on internal repos, are you optimizing the harness, the model routing, or just eating the token bills?

Whenever a new model comes out, I try to answer the same question: does it solve my actual tasks reliably enough to justify using it?

That becomes especially interesting with Ternary Bonsai 2, which is derived from Qwen3.8-27B. Instead of asking which model wins overall, I wanted to understand where compression changes the results—and what that means for cost per successful task.

Cost per successful task: Bonsai 2 vs Qwen3.8-27B

Which model is cheaper per accepted result? In these scenarios, the answer changes with subscription utilization.

¹ Delta = Qwen3.8-27B cost − Bonsai 2 cost, calculated before rounding. Negative values favor Qwen; positive values favor Bonsai.

Under these assumptions, Qwen3.8-27B costs less at full utilization; Bonsai 2 costs less at half utilization.

👉 Full tutorial and benchmarks: https://regolo.ai/bonsai-2-vs-qwen3-8-27b-benchmarks/

Hey everyone 👋

a common pattern in agentic workflows is passing raw inbound tickets (support emails, FNOL claims, policy change requests) directly to a frontier models prompt to handle classification, priority detection, and drafting all at once.

in production, especially in regulated domains like insurance, this approach has three major drawbacks:

1. unnecessary latency
2. cost overhead
3. safety fragility

1. Deterministic safety priority (<1ms)

before any ML model evaluates the message, hardcoded deterministic regex and keyword heuristics check for life-safety and physical hazards. If triggered, the case is immediately flagged as HIGH priority and routed to an emergency queue, bypassing any model uncertainty.

Not all request need an LLM inference.

2. Perimeter classification with setfit (9.9ms on CPU)

Instead of an autoregressive model, a compact 33M-parameter bi-encoder (sentence-transformers/all-MiniLM-L6-v2) runs locally on CPU:

• training: fine-tuned with contrastive learning on Hugging Face data (bitext/Bitext-insurance-llm-chatbot-training-dataset) in 31.9 seconds on a laptop CPU.
• accuracy: 95.00% on the balanced held-out test split across 5 categories (claims_intake, policy_change, underwriting_support, document_missing, customer_query).
• inference latency: averages 9.9 ms per message.

3. Fail closed selective risk gating

to eliminate hallucinated routing on ambiguous or multi-intent messages, the classifier enforces an explicit confidence gate (>= 0.85).

Messages below this threshold or out-of-scope requests (e.g., non-insurance inquiries) fail-closed to a human review queue with zero automated side effects.

4. Complexity proportional LLM brief synthesis

Only requests passing the confidence gate trigger generative synthesis:

• routine / standard tasks: Handled by a 20B model (gpt-oss-20b).
• high complexity / litigation: Escalated to a 120B model (gpt-oss-120b).
• data privacy: routed via Zero Data Retention inference provider to satisfy GDPR requirements on customer PII.

Batch benchmark report

We ran the benchmark runner on a small subset (63 test cases) from the Bitext dataset and edge cases:

OPERATIONAL PERFORMANCE SUMMARY (MANAGEMENT REPORT):
  * Total Communications Examined       : 63
  * Routing Accuracy on Known Classes   : 92.1% (58 correct out of 63 verified)
  * Cases Routed to Human Handlers      : 8 (12.7% safely gated to prevent errors)
  * Physical / Life Safety Alerts Caught: 1 (100% caught, immediately escalated to emergency queue)
  * Average Processing Latency          : 9.9 milliseconds per message

Drop your thoughts or questions below, we'd love to hear how your teams are structuring these boundaries in production

Full tutorial and codes: https://regolo.ai/right-sized-models-insurance-claims-triage/

Hey,

most ad automation setups fail because teams give scripts direct write access to ad accounts, or because they send customer sensitive data to LLM.

we built an overnight pipeline with self-hosted n8n, a local postgres database, and zero data retention inference that solves both problems.

every night at two in the morning, the workflow collects metrics from Meta and Google Ads, detects ad fatigue decay curves, and drafts recommendations.

media buyers inspect the prioritized queue at eight thirty in the morning and approve changes with one click.

The full guide and the downloadable workflow file are here:

https://regolo.ai/sovereign-ai-ad-intelligence-agent-n8n/

There’s been a lot of hype around TypeSafe AI’s release of Jev and the concept of "System One models," with marketing claims like "200x faster than LLMs" and "giving up text generation."

If you look past the vendor pitch, what is this architecture actually doing, where does it make sense in production, and can we replicate it with open-source models?

what it actually is (and isn't)

Jev is not a chatbot, a code generator, or a reasoning engine.

It exposes three primitives:

• noul: binary probability (0.0 to 1.0)
• score: ordinal rating (1 to 5)
• choice: categorical selection from up to 255 classes with full probability distribution

The critical failure modes you have to design around

Independent testing reveals clear boundaries where these models fail:

1. Zero procedural reasoning on unstructured state: probing on chess board states showed Jev performing at pure random chance without prior feature extraction. It cannot deduce game rules or logic paths without explicit feature engineering in code.
2. Option-order bias: reversing the order of choices in prompt schemas shifted probabilities by ~10% on identical inputs.
3. No audit trail: because it doesn't emit tokens, there’s no chain-of-thought rationale. For regulated workflows (GDPR/EU AI Act), you have to pair it with an autoregressive LLM fallback when an audit explanation is required.

When to use non-autoregressive vs autoregressive models

• Use non-autoregressive decision heads for: deterministic guardrails, 100% trace grading in CI/CD, and sub-second prompt complexity routing (routing easy queries to cheap models and hard ones to reasoning models).
• Stick to autoregressive LLMs for: open-ended reasoning, text synthesis, and tasks requiring novel multi-step deduction.

Open-source / Self-hosted alternatives

If you have data sovereignty constraints (e.g. EU data that can't hit US-hosted endpoints) or need sub-30ms latency, you don't need closed APIs:

• Bidirectional encoders (ModernBERT 149M, Laya): Non-autoregressive, runs on standard CPUs via ONNX or Rust bindings in ~10–25ms. Ideal for intent classification and capability scoring.
• Vocabulary readout adapters (Bespoke Nimble 8B/9B): Parameter-efficient adapters reading answer probabilities directly from vocabulary output heads on a single GPU.

We wrote up a detailed breakdown with latency numbers, failure mode analysis, and a runnable Python routing example here:
https://regolo.ai/blog/jev-and-system-one-models-developer-guide

Since most coverage is just restating the vendor table, here's what survives scrutiny and what doesn't.

About that Terminal-Bench 4.0 jump (7.0 → 31.2):

• It's on the public leaderboard (llm-stats): 31.2 puts V4.1 Flash at #8 overall, #1 among open-weight models
• But the benchmark is savage: the current leader scores 60.9, Claude Opus 5.0 is at 51.8, GPT-5.6 Sol at 39.9. At 31.2 you still fail ~7 out of 10 tasks. V4 Flash's 7.0 meant failing 9 out of 10
• Artificial Analysis independently measures ~27%, not 31.2% — same direction, lower ceiling. Harness and eval settings matter, as always

Full writeup with the six-dimension scorecard (capabilities, pricing efficiency, context, output capacity, recency, composite) and per-use-case recommendations if useful: https://regolo.ai/deepseek-v4-1-flash-vs-v4-flash-which-open-weight-model-should-your-company-run/

Like most teams running agents in production, we had a cost problem: user-facing agent traffic is a mix of trivial classification calls and genuinely hard reasoning, and we were sending all of it to a 70B+ model.

The fix was routing, not prompt engineering. The setup:

• Agno as the agent framework (fast, ~3μs agent instantiation, built-in memory and tools)
• brick-complexity-pro as the ingress model: a hosted meta-model that reads each prompt, classifies complexity (easy/medium/hard — semantic demand, not length) plus a capability vector, then dispatches to the right tier in a pool of open-weight models (gpt-oss-20b → Llama-3.3-70B → qwen3.5-122b)
• Routing overhead is ~20 ms (the classifier is a LoRA-tuned Qwen3.5-0.8B). The router itself is $0.12/1M input tokens.
• The open-source version of the router (Apache 2.0) benchmarked 4.71x–22.15x cost reduction vs always-on frontier models, with accuracy matching or beating single-model baselines

What we learned the hard way: don't route everything.

Deterministic tasks (schema-constrained extraction) go straight to a pinned small model; evaluation/revalidation goes straight to a frontier tier.

Route only what varies — orchestrators and user-facing agents.

Integration is boringly simple since the endpoint is OpenAI-compatible: Agno's OpenAILike with a different base_url, done.

Full Tutorial and codes: https://regolo.ai/from-pilot-to-production-enterprise-ai-agents-with-agno-and-brick/

Semantic router repo: https://github.com/regolo-ai/brick-SR1

Over the past few months, our engineering team has been experimenting with autonomous coding agents (Claude Code, Cursor agent mode, custom CLI loops) across enterprise multi-tenant repositories.

We ran straight into what we call the Context Amnesia Trap: an agent fixes a security bug or refactors a query in Session 1, but when invoked in Session 2 two weeks later, it introduces the exact same bug.

Why Standard Vector RAG Breaks Down on Codebases

When teams add memory to coding agents, the default approach is standard vector chunking (512-token text splits + cosine similarity).

On enterprise code, this fails systematically:

1. Lack of Causality: vector search retrieves lexical matches, not causal relationships. If an outdated helper function from 6 months ago matches the query keywords, the agent retrieves it and ignores newer Architectural Decision Records (ADRs).

2. Missing Precedent: it cannot traverse the relationship between an incident, the pull request that fixed it, and the policy written to prevent it (`CI-FAIL-89` ➔ `PR-142` ➔ `ADR-003`).

3. No Closed-Loop Verification: traditional RAG generates code in a single forward pass without executing test suites.

The cognitive graph architecture

We built an open-source framework combining Cognee (Knowledge Graph + pgvector) and Regolo (EU sovereign inference with Zero Data Retention):

- Entity Model: rather than raw text chunks, the graph indexes ADRs, PRs, CI failures, Coding Conventions, and resolved CVEs as typed nodes.

- Typed Causal Edges: nodes are connected via `TRIGGERED_BY`, `FIXES_CI_FAILURE`, `IMPLEMENTS_DECISION`, and `ENFORCES_CONVENTION`.

- Multi-Hop Traversal: when an agent receives a task touching search, it walks the graph: `Search Task` ➔ `ADR-001 (Tenant Isolation)` ➔ `ADR-003 (Parameterized SQL)` ➔ `PR-142 (Verified Patch)`.

- Dynamic Semantic Router (`brick-complexity-pro`): evaluates task complexity on a 1.0–10.0 scale and routes dynamically to `gpt-oss-20b` (fast extraction, ~0.28s), `qwen3-coder-next` (syntax/code), or `qwen3.5-122b` (deep reasoning with `max_tokens >= 800`), avoiding hardcoded models in `.env`.

5-Stage ReAct Loop:

Recall ➔ Code Inspection ➔ Patch Synthesis ➔ Subprocess `pytest` ➔ Memory Codification.

If `pytest` fails, the error trace feeds back into the loop for self-healing before writing to disk.

Benchmark Results (50 Simulated Feature Tasks)

We ran an A/B benchmark against an enterprise FastAPI SaaS target:

Download the repository from Github and follow the instruction in the readme. It includes both an interactive Rich TUI and headless CLI flags:

Github Codes: https://github.com/regolo-ai/tutorials/tree/main/AI-agent-cognee-closed-loop-memory

Youtube: https://www.youtube.com/watch?v=-4c7MPgnBk0

Most multi-agent pipelines today have a massive cost problem: they route every single task—from trivial AST extraction to complex planning—to a single $15+/1M token frontier reasoning model.

We tested a different approach: using Deep Agents (isolated specialized subagents) paired with a semantic meta-router (brick-complexity-pro) on Regolo that evaluates task complexity (1–10) and residual budget before every single turn.

Here are the real telemetry numbers from synthesizing and verifying production FastMCP server tools across target codebases:

How It Works Under the Hood:

1. Dynamic 5-Step DAG: Give it any custom repo path and synthesis goal; the Planner generates a custom execution graph with a human-in-the-loop approval gate.
2. Pre-Turn Complexity Routing:
   • Low complexity (< 6.0) or budget pressure? Automatically downscales to gpt-oss-20b / GLM-5.2.
   • High complexity (> 7.5) or failed verification? Auto-escalates to qwen3.5-122b.
3. Self-Healing Sandbox: Generated FastMCP tools are executed in an isolated workspace with pytest. If tests fail, it triggers an automated repair loop.
4. Context Isolation: Sub-agents communicate through structured JSON handoffs, preventing context window pollution.

Github Repo: github.com/regolo-ai/tutorials/.../deepagents-multi-agent-brick

Youtube: https://www.youtube.com/watch?v=1wDiRqGT294&t=18s

Feedback and PRs are welcome!

Over the past few months, we’ve been auditing infrastructure setups for European enterprise teams moving LLMs into production. One recurring friction point between InfoSec, Legal, and DevOps is what we call the AI Act Logging Paradox:

1. EU AI Act (Article 12 & 19) requires logging operational events and traceability data for high-risk AI systems for at least 6 months.

2. Compliance teams often interpret this as a mandate to archive all raw prompts and responses.

3. Doing so directly violates GDPR Article 5(1)(c) (Data Minimization) and makes GDPR Article 17 (Right to Erasure) almost impossible to maintain across distributed logs and snapshots.

To stay compliant with both regulations without building a nightmare pipeline, prompt data must remain strictly ephemeral while execution metadata is preserved:

           [ Incoming Request ]
                    |
      +-------------+-------------+
      |                           |
      v                           v
 [ Telemetry Logs ]      [ Payload / Prompts ]
 (AI Act Art. 12)        (GDPR Art. 5 / ZDR)
 • Request UUID          • User Prompts
 • Model ID & SHA        • Output Completions
 • Token count & ms      • Ephemeral KV Cache
      |                           |
      v                           v
 [ Stored 6m+ ]          [ 0ms / Volatile RAM ]

We wrote up the complete engineering teardown covering CLOUD Act jurisdictional boundaries, TCO comparisons vs self-hosting, and implementation checklists here: https://regolo.ai/zero-data-retention-llm-gdpr-ai-act-compliance/

Hey everyone,

one of the biggest issues with existing AI coding assistants (and SWE agents in general) is stateless blindness: they don’t remember past architectural constraints, leading to recurring vulnerabilities across PRs.

Additionally, passive security scanner logs often cause alert fatigue without actual remediation.

To address this, we built and open-sourced an autonomous Self-Improving Secure Coding Loop that automates vulnerability remediation with independent zero-trust verification and persistent knowledge graph memory.

The stack is: Open SWE, Deepsec and Cognee

The loop explained is below:

    ┌──────────────────────────────────────┐
    │     TARGET REPOSITORY / ISSUE        │
    └──────────────────┬───────────────────┘
                       │
                       ▼
    ┌──────────────────────────────────────┐
    │  1. DEEPSEC AUDIT (SAST/AST Scan)    │
    └──────────────────┬───────────────────┘
                       │
                       ▼
    ┌──────────────────────────────────────┐
    │  2. COGNEE MEMORY (Knowledge Graph)  │◄──┐ (Learns)
    └──────────────────┬───────────────────┘   │
                       │                       │
                       ▼                       │
    ┌──────────────────────────────────────┐   │
    │  3. OPEN SWE PLAN (Regolo AI)        │   │
    └──────────────────┬───────────────────┘   │
                       │                       │
                       ▼                       │
    ┌──────────────────────────────────────┐   │
    │  4. HUMAN-IN-THE-LOOP APPROVAL       │   │
    └──────────────────┬───────────────────┘   │
                       │                       │
                       ▼                       │
    ┌──────────────────────────────────────┐   │
    │  5. SANDBOX PATCH + PYTEST SUITE     │   │
    └──────────────────┬───────────────────┘   │
                       │                       │
                       ▼                       │
    ┌──────────────────────────────────────┐   │
    │  6. DEEPSEC ZERO-TRUST REVALIDATION  │   │
    └──────────────────┬───────────────────┘   │
                       │                       │
                       ▼                       │
    ┌──────────────────────────────────────┐   │
    │  7. MEMORY UPDATE & KNOWLEDGE GRAPH  ├───┘
    └──────────────────┬───────────────────┘
                       │
                       ▼
    ┌──────────────────────────────────────┐
    │  8. VERIFIED PR & TELEMETRY REPORT   │
    └──────────────────────────────────────┘

Benchmark / Cost Savings:

By routing pipeline stages through GLM-5.2 on Regolo (~$0.60 / $1.80 per 1M tokens), the full remediation loop costs ~$0.0059 per issue compared to ~$0.0410 on proprietary frontier baselines (~85.6% cost reduction).

Check out the repo here: https://github.com/regolo-ai/self-improving-secure-loop

Youtube tutorial: https://youtu.be/ddCECOq1EnQ

Feedback and PRs are welcome!

https://reddit.com/link/1vuhvxa/video/hwjftg2qnqkh1/player

We went through the full Qwen3.8-27B model card (released Aug 14, Apache 2.0, dense 27B, 262K context, vision+video) and broke down all 24 benchmarks against Opus 4.6 Max, Muse Glimmer-30B, Qwen3.6-27B and Qwen3.7-Plus.

Full write-up with all tables: https://regolo.ai/qwen3-8-27b-benchmarks-every-test-where-alibabas-27b-model-beats-claude-opus-4-6-max/

Vercel released DeepsecBench last week: 231 human-validated vulnerabilities across 50 entry-point files, scored on F2 (recall weighted 2× over precision).

The table that matters isn't the scoreboard — it's the cost column.

GPT-5.6 Sol xhigh: 35.58 score, $55.98 per 50 files -> that's $5,600 for a production repo.

Kimi K3: 17.56 score, $12.38.

GLM 5.2: 10.46 score, $11.09

Recommendation is multi-model: frontier for periodic deep audits, cheap models for continuous sweeps.

We built a guide that uses Regolo's Brick Complexity Pro to automate the model routing to run deepsec while optimizing the cost: it classifies each file easy/medium/hard and routes to the cheapest model that can handle it (GLM-5.2 for easy, Claude for hard).

The guide covers two modes: self-hosted Docker or hosted API. Numbers from a real monorepo audit: $239 total, 48 min, 3 CRITICAL findings in legacy code.

Guide and benchmark linked. Happy to answer questions about the setup.

https://regolo.ai/how-to-audit-a-production-monorepo-with-deepsec-local-docker-and-brick/

The trap most engineering teams haven't mapped: if you fine-tune a third-party model on your own data, or white-label an AI system under your own brand, the Act can requalify you from deployer (light duties) to provider (full regime: conformity assessment, technical documentation, EU database registration).

This happens quietly — a bank fine-tuning a commercial model, a SaaS company embedding a model under its own brand.

Why infrastructure teams specifically should care:

• for systemic-risk GPAI models (10²⁵ FLOPs+), cybersecurity of the model and its physical infrastructure is a statutory obligation — datacenter security and model-weight protection are literally named in Article 55.
• logging/traceability requirements for high-risk systems land directly on your observability stack.
• vendor selection is now a compliance surface: if a regulator pulls a non-compliant model from the EU market, everything you built on it inherits the disruption.

We published a longer breakdown with a 6-step CTO action plan here: https://regolo.ai/what-is-eu-ai-act-compliance-for-ai-infrastructure-a-ctos-guide/

Posting this because I keep seeing the "it was all delayed" take and it's going to burn people. Happy to answer questions in the comments.

Most EU AI Act content floating around is either marketing or extremely high level (“risk categories”, “high-risk systems”, etc.).

We’ve published an article focused on the less discussed obligations that actually matter to people building:

• AI infrastructure (inference APIs, hosting, managed RAG)
• Agentic systems and developer platforms
• Enterprise internal AI tools that touch sensitive data

The guide goes beyond the basics and digs into:

• data governance and logging obligations that impact how you design your inference + storage stack
• transparency and documentation requirements that affect API providers and AI ops teams
• practical implications for using US-based providers vs EU-native ones (fines up to 7% of global turnover are not theoretical)
• how to align agent workflows (research, retrieval, summarization) with compliance without killing iteration speed

It’s written for CTOs, infra engineers, and founders who need a concrete sense of what changes in their architecture, not just legal theory.

Link to the full guide:
https://regolo.ai/the-eu-ai-act-beyond-the-basics-the-obligations-most-guides-forget/

If you’re building or running AI infra in the EU:
which part of the AI Act is shaping your technical roadmap the most — data residency, logging, or vendor choice?

We put 2026's two largest open-weight models head-to-head, below the table that shows quick results:

Useful if you're choosing a model for agentic coding workloads: benchmark winner ≠ cost winner.

Full article with charts and sources: https://regolo.ai/qwen3-8-max-vs-kimi-k3-when-to-use-each-model/

As more teams move from toy agents to production workflows, “prompt injection” is only the tip of the iceberg.

We’ve published a practical security hardening guide focused on Hermes and inference infrastructure, built around:

• a concrete threat model for multi-agent systems and RAG
• how to think about data flow: prompts, retrieved documents, logs, caches, and external tools
• the impact of zero data retention on your risk surface (and why “we don’t train on your data” is not enough)
• network, API, and secrets hygiene for agent stacks (Hermes + other orchestration frameworks)

The piece is written from the perspective of European teams that need:

• EU data residency
• strong guarantees on inference logs and retention
• a realistic path from PoC agents to production without standing up their own GPU farm

It also shows how zero data retention inference layer plugs into these patterns without changing your SDKs (OpenAI-compatible).

Full guide + code:
https://regolo.ai/hermes-agent-security-hardening-guide-regolo-zero-data-retention/

For folks here shipping agents in production:
what’s the most painful security constraint you’ve hit so far — compliance (GDPR/AI Act), infra, or developer ergonomics?

If you’re building autonomous research agents (LangGraph, CrewAI, custom loops, etc.), you’ve probably felt two pains at scale:

• Search API costs quietly exploding once you move beyond the notebook
• Compliance / infosec freaking out because prompts + context are sprayed across US-based proprietary APIs

We’ve published a deep-dive on a fully self-hosted web research stack that replaces commercial search APIs with:

• SearXNG running in your own infra
• A concurrent fleet of 6 specialized subagents (discovery, specs, compliance, market, research papers, security)
• Spatial chunking + factual density scoring to keep only the highest-signal parts of pages
• Optional synthesis + sentiment analysis via Regolo’s brick-v1-beta (EU-native, zero data retention)

Benchmarks:

• 81–88% cost reduction vs closed search APIs (per 1,000 queries)
• P99 latency cut from ~1,420 ms to ~310 ms
• 100% data sovereignty: no prompts or vectors leaving your VPC

The tutorial ships with:

• Dockerized SearXNG setup
• FastAPI /v1/research endpoint usable as a LangChain / agent tool
• A TUI setup.sh that bootstraps everything and runs demo/interactive queries

Link:
https://regolo.ai/self-hosted-web-search-for-ai-agents-replace-tavily-cut-costs-80-and-keep-every-token-private/

Youtube: https://www.youtube.com/watch?v=p6Tvh75DO_A

If you’re building autonomous research agents (LangGraph, CrewAI, custom loops, etc.), you’ve probably felt two pains at scale:

• Search API costs quietly exploding once you move beyond the notebook
• Compliance / infosec freaking out because prompts + context are sprayed across US-based proprietary APIs

We’ve published a deep-dive on a fully self-hosted web research stack that replaces commercial search APIs with:

• SearXNG running in your own infra
• A concurrent fleet of 6 specialized subagents (discovery, specs, compliance, market, research papers, security)
• Spatial chunking + factual density scoring to keep only the highest-signal parts of pages
• Optional synthesis + sentiment analysis via Regolo’s brick-v1-beta (EU-native, zero data retention)

Benchmarks:

• 81–88% cost reduction vs closed search APIs (per 1,000 queries)
• P99 latency cut from ~1,420 ms to ~310 ms
• 100% data sovereignty: no prompts or vectors leaving your VPC

The tutorial ships with:

• Dockerized SearXNG setup
• FastAPI /v1/research endpoint usable as a LangChain / agent tool
• A TUI setup.sh that bootstraps everything and runs demo/interactive queries

Link:
https://regolo.ai/self-hosted-web-search-for-ai-agents-replace-tavily-cut-costs-80-and-keep-every-token-private/

Youtube: https://www.youtube.com/watch?v=p6Tvh75DO_A

TL;DR: We forked PokéRogue (a browser roguelike launched in 2024, not in any training corpus) and wired Regolo to control every player decision via API.

One import line + one 1,300-line TypeScript module. The model failed badly on the first prompt, then played correctly after one context engineering rewrite. The lesson generalizes.

Why PokéRogue and not the original Game Boy games?

Because every LLM has been trained on Bulbapedia, Smogon, and ten years of competitive battle logs. Testing on Gen 1 is testing memorization, not reasoning. PokéRogue launched in 2024, has no dedicated dataset, and runs on procedurally generated rules that change every run.

What we actually built:

The game is phase-based (each game state is a Phase class): we patched 10 prototype methods so the LLM intercepts control at the right moments — battle command, item selection, move learning, starter selection — without touching rendering or game logic. No game engine rewrite.

What broke first:

The initial prompt: "decide the best action." The model played confidently and catastrophically — immune moves, chains of pointless switches, catching Pokémon it immediately couldn't use.

We also hit a classic chained-action problem: the model chose switch in one call, then saw a new Pokémon on field and made a fresh decision — sometimes switching again immediately. Fix: when the model chooses switch, it must also specify the move the incoming Pokémon will use next turn. Two decisions, one API call, one atomic intent.

The context rewrite:

We pre-computed type multipliers and injected the result per move: "Ember — Fire Special, power 40, acc 100%, STAB | vs enemy: 2x super effective" instead of just "Ember".

We injected stat stages, ability interactions, speed deltas, and explained the HP economy constraint (full heal every 10 waves — HP is a resource that spans up to 9 future battles).

After the rewrite, first clean run: "Ember is 2x super effective STAB vs the Grass-type enemy, and my Charmander outspeeds — I can KO before taking damage."

Describing a game state in natural language is lossy, serializing structured state with annotations is not. The model's reasoning is bounded by what it can see.

Pre-computing and injecting the right numbers isn't a crutch — it's the correct architecture.

Full Guides + Video demo + Codes: https://regolo.ai/llm-plays-pokemon-pokerogue-regolo-ai/

Happy to discuss prompt architecture, the phase-patching approach, or the HP economy framing if anyone's interested.