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The Škoda Peaq, which will debut this summer as the European automaker’s flagship model, seats seven and has an intriguing design. It will almost certainly come with a hefty price tag.

Extends an earlier model-selection benchmark to three model families (Japanese / Western / Chinese) on a Japanese RAG task. Repo + raw results: https://github.com/elvisyao007/eval-driven-llm/tree/main/reports/model-selection-v2 An earlier post benchmarked local models for a Japanese RAG task and settled on selecting by constraint rather than raw capability. This post widens the field to three families — Japanese-tuned, Western open, and Chinese — and the result forces a distinction that matters more than any single score: model capability and deployment eligibility are two different questions, and conflating them is how people get model selection wrong. Same Japanese RAG task, same judge protocol, same discriminating golden set (oracle 87.5%, only 11% of questions answered by all models — it actually separates the field). hit@5, 8B class unless noted: Model Family hit@5 Swallow-8B Japanese-tuned ~0.53 Nemotron-9B-JP Japanese-tuned ~0.62 ELYZA-JP-8B Japanese-tuned ~0.40 deepseek-r1-8b Chinese ~0.51 Llama-3.1-8B Western ~0.22 Mistral-7B Western ~0.18 gemma4-31b Western (31B) ~0.62 Three things fall out of this, and they don't all point the same direction. 1. At 8B, Japanese fine-tuning is decisive — and generic Western models just aren't competitive The Western 8B models cratered: Llama-3.1-8B at 0.22, Mistral-7B at 0.18, against a Japanese-tuned average around 0.52. That's not a small gap; it's the difference between usable and not. This answers a question people sometimes ask skeptically — why do Japanese-specific models exist when Llama is right there? At the 8B scale, on a Japanese retrieval-grounded task, a generic Western model without Japanese fine-tuning is not in the running. The Japanese tuning is doing decisive work. One honest qualifier on the table: gemma4-31b (0.62) is the one Western model that holds up — but it's 31B, not 8B. It earns its score with 4× the parameters, not with Japanese optimization. So read the table in two tiers: within the 8B class,

Three days. Claude Fable 5 — Anthropic's most capable model ever shipped to the public, posting 95% on SWE-bench Verified — was live for exactly 72 hours before the US government issued an export control directive on June 12 that forced Anthropic to pull it globally. For everyone. Including US users. Including Anthropic employees who hold foreign passports. Here is what Fable 5 actually is, what the government directive says, what Anthropic says about it, and what developers building on Claude should do while this gets resolved. What Claude Fable 5 Is Anthropic launched Claude Fable 5 on June 9, 2026, alongside Claude Mythos 5, its restricted sibling for government-adjacent cybersecurity work. Fable 5 is the first publicly available model in Anthropic's new "Mythos-class" tier — a category above the previous frontier that Claude Opus 4.8 (released May 28) occupied. The benchmark gap is not close. Fable 5 posted 95.0% on SWE-bench Verified and 80.3% on SWE-bench Pro. The next best competitor on SWE-bench Pro is GPT-5.5, sitting at 58.6%. That is a 21.7-point gap — roughly twice the margin by which Claude Opus 4.8 led its generation. Across all eight coding benchmarks Anthropic published at launch, Fable 5 led with an average margin of 11.8 points: Benchmark Claude Fable 5 Next Best Gap | SWE-bench Verified | 95.0% | ~74% | +21 | | SWE-bench Pro | 80.3% | 58.6% (GPT-5.5) | +21.7 | | FrontierCode Diamond | leads | baseline | +23.6 | | HLE (no tools) | leads | baseline | +13.7 | | Terminal-Bench | leads | baseline | +4.6 | Beyond static benchmarks, Anthropic ran a long-horizon game-playing evaluation using Slay the Spire with persistent file-based memory. Fable 5 improved three times faster than Opus 4.8 as memory accumulated, and reached the final act three times as often. The large-context reasoning advantage — the same capability that powered the 8x engineering productivity multiplier at Anthropic — is structurally more pronounced in Fable 5 than in any previous publ

What UAE BNPL providers need to verify under CBUAE short-term credit rules, which income sources matter, and how automated income verification fits compliance.

From Demo Code to a Reusable Package Article 19 used a 900-line harness_full_demo.py to demonstrate eight defense layers. That file is good for explaining concepts, but not for reuse — all layers are coupled together, nothing can be tested in isolation, and nothing can be imported by another project. A production-grade Agent project needs something you can actually import : harness/ ├── __init__.py Public API exports ├── registry.py Layer 2: ActionRegistry + PermissionLevel ├── budget.py Layer 3: PermissionBudget (with refund()) ├── sandbox.py Layer 4: sanitise_input + sandboxed_eval ├── audit.py Layer 6: ImmutableAuditLog (hash-chained) ├── rollback.py Layer 7: RollbackCoordinator └── harness.py Unified entry point: AgentHarness This article starts with package design, covers three key API decisions, and finishes with two integration styles: standalone Python and LangGraph graph embedding. Module Design registry.py — Layer 2 class PermissionLevel ( Enum ): READ = 1 WRITE = 2 ADMIN = 3 IRREVERSIBLE = 4 @dataclass class RegisteredAction : name : str level : PermissionLevel budget_cost : int description : " str " handler : Any # Callable or BaseTool class ActionRegistry : def register ( self , action : RegisteredAction ) -> None : ... def get ( self , name : str ) -> RegisteredAction : ... # not found → PermissionError def is_allowed ( self , name : str ) -> bool : ... def names ( self ) -> list [ str ]: ... get() rather than __getitem__ : raises a consistent PermissionError , without leaking the internal KeyError detail. budget.py — Layer 3 class PermissionBudget : def spend ( self , action_name : str , cost : int ) -> None : if self . remaining < cost : raise BudgetExhaustedError (...) self . remaining -= cost def refund ( self , action_name : str , cost : int ) -> None : self . remaining = min ( self . total , self . remaining + cost ) The new refund() method fixes a design flaw from Article 19: budget was deducted before approval, and never returned on rejection.

Traditional databases just can't keep up with high concurrency and low latency at the same time. The term "real-time" has become kind of meaningless. Everyone claims it, from batch-oriented cloud data warehouses to transactional database extensions. This makes picking the right architecture really hard without expensive trial and error. The best real-time analytics database in 2026 depends entirely on your workload shape. Key takeaways Real-time analytics (in this guide) = sub-second p95/p99 analytical queries on billions of rows, high concurrency , and milliseconds-to-seconds freshness . Best overall in 2026 for most workloads: ClickHouse (ingest throughput, query speed at scale, compression/TCO). Best for strictly predefined query paths via star-tree indexes: Apache Pinot . Best for time-series operational dashboards and observability: ClickHouse . ClickStack is its full observability offering for logs, metrics, and traces. Best for rigid ingestion-time roll-up aggregations: Apache Druid . Best for unified OLTP + real-time analytics: ClickHouse paired with its managed Postgres offering and native sync to ClickHouse , giving you a purpose-built OLTP engine and a purpose-built OLAP engine without rolling your own CDC pipeline. SingleStore is an alternative if you prefer a single HTAP engine for both. Traditional Data Warehouses: Snowflake and BigQuery are fine for batch BI if you already have one, but face latency, concurrency, and cost challenges under sub-second, high-concurrency workloads. Evaluate using 4 axes: ingest/freshness, latency under concurrency, TCO, operational complexity. What 'real-time analytics' means (and why warehouses and OLTP databases fail) Strict engineering thresholds define true real-time OLAP : sub-second query latency on complex aggregations, the ability to serve tens to thousands of concurrent queries per second (QPS), and data freshness measured in milliseconds to seconds. Traditional cloud data warehouses like Snowflake and BigQuery a

Tech leaders debate whether the Anthropic episode is a wake-up call for India’s AI ambitions.

Meta starts dismantling its $2 billion Manus acquisition after Beijing ordered the deal reversed.

The hype cycle for AI adoption in businesses often follows a familiar, and often frustrating, trajectory. It begins with the undeniable allure of a powerful new tool—seeing ChatGPT effortlessly summarize documents or Copilot intelligently autocomplete code is undeniably impressive. The immediate reaction is almost universally: "We need to get AI." So, tools are procured, workshops are run, and initial enthusiasm soars. Yet, more often than not, six months down the line, that excitement has waned, and the needle on actual business transformation hasn't moved. The fundamental operational rhythm of the organization remains unchanged. As a Senior IT Consultant and Digital Solutions Architect with over a decade of experience, I've observed this pattern repeatedly across various client engagements. This isn't a failure of the technology itself, but rather a profound strategy failure . It’s the single most common and costly mistake I encounter in AI adoption today. The Critical Distinction: Tool Acquisition vs. Strategic Integration Think of buying an AI tool like purchasing a state-of-the-art machine for a workshop. Its inherent value is immense, but if it sits in a corner, unused or without a defined process around it, that value remains entirely theoretical. It's an expense, not an asset generating return. Real AI integration , on the other hand, is a disciplined, multi-faceted endeavor. It's about deeply understanding your existing business processes, meticulously identifying precisely where intelligent automation can create genuine, measurable leverage. It involves designing a holistic system that delivers this leverage, and critically, establishing robust mechanisms to measure the actual outcomes against predefined business objectives. This holistic approach is strategy. The tools—be it an LLM, a specific AI platform, or an automation suite—are merely components that strategy carefully selects and orchestrates to achieve a greater aim. Where AI Integration Truly Crea

According to the Wall Street Journal, the export control directive that led to Anthropic cutting off access to Fable 5 and Mythos 5 was triggered in part by cybersecurity research from Amazon and conversations between CEO Andy Jassy and the White House. According to the report, the paper from Amazon claims that, through a series […]

HashiCorp has announced the general availability of the Terraform MCP Server, an open-source MCP server that enables agents to integrate with Terraform Registry APIs. The company says that it can improve infrastructure teams productivity by relieving engineers of rote tasks. By Sergio De Simone

In 2026, Marvel and Mattel are both releasing projects designed to capitalize on people's love for iconic animated heroes from their childhoods. Masters of the Universe has put a live-action He-Man on the big screen, and the second season of X-Men '97 is about to fling some of Charles Xavier's mutants into an apocalyptic future. […]

Amazon CEO Andy Jassy may have been the source of security concerns that led Anthropic to cut off worldwide access to two models on Friday.

I turned off Siri on the Mac years ago and never looked back. Similarly, I found Apple Intelligence so fruitless I never engage with it. But the new Siri AI coming to macOS 27 Golden Gate has at least got me slightly rethinking things. I'm still early in testing Siri AI, as I've only had […]

On Friday evening, the government ordered Anthropic to block access to Fable 5 and Mythos 5 for all foreign nations, both inside and outside the US, due to national security concerns. That order included employees of Anthropic. To meet those demands, the company has completely cut off access to the models for all customers. In […]

If you have made it this far in 100 Days of Solana, you have been working in JavaScript and on the command line. You have been calling RPC methods, building instructions, signing transactions, and reading and writing account data in JavaScript, and most recently minting and sending tokens and NFTs from the CLI. Either way, you have been driving Solana with tools that let you assign a value and move on with your life. Soon the ground shifts. You are going to open a file called lib.rs , and it is going to be Rust, and for a day or two it is going to feel like you forgot how to program. That feeling is normal, it is temporary, and it is not a sign you are in the wrong place. Here is the thing nobody says out loud: you do not need to learn all of Rust to write Solana programs. Rust is a big language with a steep reputation, but the slice of it that shows up in an Anchor program is small and repetitive. You will see the same handful of patterns on almost every line. Learn those patterns and the wall turns back into a floor. This post is that handful. Not a Rust course, just the parts you need to read your first Anchor program and understand what every line is doing. Next week we start Arc 9, the Anchor introduction, where this all becomes real. This week is about making the language stop being scary before you get there. Why it feels like a wall JavaScript is dynamically typed and garbage collected. You write const x = 5 , you never tell anyone it is a number, and when you are done with it the runtime quietly cleans up. The language trusts you and sorts out the consequences at runtime, which is why a typo surfaces as undefined is not a function three minutes into a demo. Rust is the opposite philosophy. It is compiled and statically typed, so every value has a type the compiler knows about before the program ever runs, and it has no garbage collector, so it tracks who is responsible for every piece of memory through a system called ownership. The trade is blunt: Rust mak

The rest of Dyson’s promised 2026 vacuum lineup is here, from the new Dyson V16 Piston Animal to an updated version of the favored Dyson V8 Cyclone.

As World Cup action kicks off, we look at the physics of the beautiful game.

Plus: AI bug hunting fuels Microsoft’s biggest-ever Patch Tuesday, ShinyHunters ransomware gang exploits an Oracle zero-day, and more.

RJ Scaringe, the CEO of Rivian Automotive, joined us for a wide-ranging interview about how his company’s new electric SUV fits into the current EV industry, and what comes next.