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figma2 min readCurated summary

Improving Performance in the Layers Panel | Figma Blog

Figma rebuilt its layers panel to handle files containing tens of thousands of layers. The old architecture recomputed too much data too often, slowing both panel interactions and broader editor operations. A two-pass computation model and cached derived properties now make some interactions 30–50% faster. ## Why the Original Architecture Slowed Down - Figma files are trees of nodes with properties and children. - The panel previously built a large JavaScript object in one recursive pass. - Every expanded node—and often all of its descendants—had its display data recomputed after changes. - This created two problems: - Data was computed for hundreds of thousands of nodes even though only 20–30 rows were visible. - Small changes, such as expanding a node, triggered broad recomputation because incremental results were rarely cached. ## Two-Pass Computation - The first pass computes only the ordered list of row IDs shown in the panel. - Determining that order still requires handling complex rules, including: - Reversed child ordering in autolayout frames. - Node types such as widgets and FigJam stickies that hide children. - Fixed and scrolling headers that divide prototype-frame children. - Sticky top-level frames and components. - The second pass gathers display data—names, icons, lock and visibility state, and selection state—only for rows inside the visible window. - This makes windowing effective: previously, Figma computed data for off-screen rows even though they were not rendered. ## Caching Derived Data - Figma introduced its “derived properties” platform primitive to avoid recomputing unchanged data. - Nodes store mutable fields, while other values are calculated from those fields and related properties. - For example, a node’s absolute position can be derived from its parent’s absolute position and its relative position: ```text Self.AbsolutePosition = Parent.AbsolutePosition + Self.RelativePosition ``` - Derived properties: - Track their dependencies through an optimized dependency graph. - Support different caching strategies balancing speed and memory. - Are lazy by default, computing values only when they are read. - Because the layers panel is itself a tree, this dependency-aware system lets Figma update only affected rows while keeping stable portions cached. Figma’s results show the value of combining virtualization with incremental, dependency-based computation: large hierarchical interfaces can remain responsive when they calculate only visible data and preserve everything that has not changed.

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github3 min readCurated summary

Give GitHub Copilot CLI real code intelligence with language servers

GitHub Copilot CLI can understand code far more accurately when connected to a Language Server Protocol (LSP) server. Without LSP, it relies on grep, package-directory browsing, and bytecode extraction, which can miss types, overloads, and dependencies. The LSP Setup skill automates server installation and configuration for 14 languages, giving the CLI capabilities such as type resolution, go-to-definition, and reference search. ## The Problem with Heuristic Code Understanding - Without an LSP server, Copilot CLI may: - Extract Java JAR files and grep through `.class` files. - Read installed Python packages directly. - Search through TypeScript’s `node_modules`. - These approaches use text and pattern matching rather than semantic analysis. - They often fail to correctly understand: - Generics and overloads. - Transitive types. - Compiled dependencies. - Exact method signatures and symbol relationships. - LSP requests such as `textDocument/definition` return precise source locations, resolved types, and signatures. ## How the LSP Setup Skill Works The skill automates a seven-step process: - **Language selection:** Uses `ask_user` to determine the required language. - **Operating system detection:** Identifies macOS, Linux, or Windows so it can choose the correct installation commands. - **Server lookup:** Reads curated data for 14 languages from `references/lsp-servers.md`. - **Configuration scope:** Supports: - User-wide configuration at `~/.copilot/lsp-config.json`. - Repository-specific configuration at `lsp.json` or `.github/lsp.json`. - Repository configuration takes precedence. - **Installation:** Runs the appropriate package-manager or platform-specific command, such as: - `npm install -g typescript typescript-language-server` - `brew install jdtls` - `rustup component add rust-analyzer` - **Configuration:** Adds a server under the `lspServers` object, mapping commands and file extensions to language identifiers. - **Verification:** Confirms the executable is on `PATH` and checks that the configuration is valid JSON. The skill merges new settings with existing configuration instead of overwriting other servers. It also accounts for transport differences, such as servers requiring `--stdio`. ## Supported Languages and Custom Setup - The skill provides predefined installation and configuration details for 14 languages. - If a language is not included, Copilot CLI can search for a suitable server and guide the user through manual configuration. - Each server configuration specifies: - The executable command. - Optional arguments. - File-extension mappings such as `.java` to `java`. ## Benefits After Configuration With LSP enabled, Copilot CLI can: - Resolve types across external dependencies. - Jump to definitions in third-party libraries. - Find every reference to a symbol. - Display hover documentation for functions, classes, and types. - Reduce unnecessary tool calls and avoid incorrect assumptions about APIs. - Handle larger and more complex coding tasks with IDE-like semantic understanding. ## Getting Started - Download the LSP Setup skill from the Awesome Copilot project. - Extract it into `~/.copilot/skills/`. - Restart Copilot CLI. - Ask the agent to set up LSP for a language, such as Java or Python. - Restart the CLI again, run `/lsp`, and test navigation on a dependency symbol. The practical recommendation is to configure an LSP server for each language used in a project. The setup gives Copilot CLI structured code intelligence instead of forcing it to reconstruct APIs through text searches and binary inspection.

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aws3 min readCurated summary

Now available: Amazon EC2 M9g and M9gd instances powered by new AWS Graviton5 processors | Amazon Web Services

AWS has generally released Amazon EC2 M9g and M9gd instances, powered by its Graviton5 processors. They deliver higher compute, memory, networking, and storage performance while improving energy efficiency and security. M9g targets general-purpose workloads, while M9gd adds up to 11.4 TB of local NVMe SSD storage for low-latency applications. ## Graviton5’s Performance Improvements - Graviton5 provides up to: - 25% better compute performance than Graviton4 - 35% faster web applications and ML inference - 30% faster database workloads - It includes: - 192 CPU cores - Five times more L3 cache - Up to 33% lower inter-core latency - DDR5-8800 memory - PCIe Gen6 support - These features improve concurrency, memory bandwidth, and CPU-heavy workloads such as agentic AI, code generation, real-time reasoning, and multi-step orchestration. - Customer results include: - ClickHouse: 36% better performance than M8g without code changes - Honeycomb: 36% higher throughput per core - HubSpot: up to 60% shorter MySQL query duration ## Networking and Storage Enhancements - M9g and M9gd provide: - Up to 15% higher network bandwidth - Up to 20% higher EBS bandwidth on average - Up to twice the network bandwidth on the largest instance - Instance Bandwidth Configuration lets customers shift bandwidth between EBS and VPC networking by up to 25%. - This is useful for databases, query processing, logging, and other workloads with demanding I/O requirements. ## Nitro Isolation Engine - M9g and M9gd introduce the Nitro Isolation Engine, an enhancement to the AWS Nitro System. - It isolates virtual machines by controlling access to: - Memory - CPU register state - I/O devices - The component uses formal verification to mathematically prove isolation behavior rather than relying solely on testing. - AWS describes Nitro as the first formally verified cloud hypervisor. ## M9g General-Purpose Instances - M9g instances provide one vCPU per 4 GiB of memory. - Suitable workloads include: - Application servers and microservices - Web applications and containerized applications - Midsize data stores and caching fleets - Gaming servers - Large-scale Java applications - Code repositories - Agentic AI workloads ## M9gd Instances with Local NVMe Storage - M9gd adds up to 11.4 TB of local NVMe SSD storage. - It offers up to 30% higher IOPS and storage performance than Graviton4-based M8gd instances. - Recommended for: - Low-latency caches and scratch files - Data and log processing - Media processing - Batch workloads - Key-value stores - Gaming and application servers ## Availability and Migration Resources - M9g and M9gd are available in: - US East (N. Virginia) - US East (Ohio) - US West (Oregon) - Europe (Frankfurt) - Purchasing options include Savings Plans, On-Demand, Spot Instances, Dedicated Instances, and Dedicated Hosts. - AWS provides a Graviton Getting Started Guide, Graviton Savings Dashboard, and AWS Transform for migrating Java applications from x86 to Graviton. Organizations seeking better compute efficiency, higher I/O throughput, or lower-cost Arm infrastructure should evaluate M9g for general-purpose workloads and M9gd when local NVMe storage is required.

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cloudflare3 min readCurated summary

Route public traffic to private applications with Cloudflare

Cloudflare is extending its application security, performance, and programmability services to applications hosted on private networks. Its new Application Services for Private Origins allows public traffic to reach private origins through existing connectivity such as IPsec, GRE, Cloudflare Tunnel, CNI, or Cloudflare Mesh—without public IP exposure, inbound firewall rules, or `cloudflared` on the origin. The feature is entering closed beta for eligible Enterprise customers. ## Unifying Public and Private Application Traffic - Private applications—including internal APIs, AI backends, MCP servers, and operational tools—can now use: - WAF - Bot management - Rate limiting - Caching - Traffic acceleration - Rewrites - Workers - Cloudflare treats private IPs as valid origin targets for public hostnames. - The model supports four traffic combinations: - Public users to public applications - Private users to public applications - Public users to private applications, which is shipping now - Private users to private applications, planned for the future ## Reusing Existing Private Connectivity - The feature builds on Cloudflare’s existing private networking layer. - Supported connectivity models include: - Cloudflare Tunnel - Cloudflare One Client - IPsec and GRE tunnels - CNI links - Cloudflare Mesh - Customers can manage routing through Cloudflare’s dashboard and APIs rather than maintaining separate networking stacks. - Workers VPC bindings and Spectrum private-origin routing also use this shared connectivity layer. ## Application Services for Private Origins - Customers can enable **Use private network routing** on a proxied A or AAAA DNS record. - Cloudflare continues applying WAF, rate limiting, caching, bot management, and transform rules at its edge. - Only the final connection differs: Cloudflare sends traffic through the customer’s private network instead of over the public Internet. - Private routing is automatically enabled for: - RFC 1918 IPv4 ranges such as `10.0.0.0/8` - RFC 6598 CGNAT ranges such as `100.64.0.0/10` - RFC 4193 IPv6 unique-local addresses such as `FC00::/7` - Public IPs reachable only through a private network can be configured manually. ## API Configuration - Private routing is represented as an additional DNS record attribute: ```json { "type": "A", "name": "app.example.com", "content": "10.0.0.50", "ttl": 300, "proxied": true, "use_private_routing": true } ``` - Cloudflare’s Origin API returns the `use_private_routing` flag. - When the proxy sees that flag, it passes the request to Cloudflare’s private networking layer, which selects the appropriate private path. ## Beyond HTTP - The same routing approach supports non-HTTP services. - Potential origins include: - TCP databases - UDP logging endpoints - Private APIs accessed by Workers - Spectrum can extend the model to TCP and UDP services, while Workers VPC enables direct access from serverless code. Cloudflare’s recommendation is effectively to use existing private connectivity as the transport layer while centralizing application security, performance, and routing at Cloudflare’s edge. The feature is currently limited to eligible Enterprise customers in closed beta.

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naver1 min readCurated summary

Preventing Unintended Changes to Android Apps

The content is a minimal NAVER D2 page rather than a substantive technical blog post. It contains a greeting, links to NAVER developer resources and initiatives, and a copyright notice, but provides no technical argument or conclusion. ## Page Contents - “Hello world” appears as the main greeting. - Navigation links include: - D2 News - About D2 - NAVER Developers - DEVIEW - OpenSource - D2 STARTUP FACTORY - The page ends with: “Copyright © NAVER Corp. All Rights Reserved.” No technical details or recommendations are provided.

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line4 min readCurated summary

Transitioning from a Legacy Project to an AI-Driven Project: The AX Roadmap

AI transformation (AX) is not achieved by simply adding AI tools; it requires redesigning the team’s development system around AI. The post proposes a four-stage roadmap for turning legacy projects into AI-driven projects, beginning with security and standardization and progressing toward specification-based development automation. Its central recommendation is to introduce AI gradually, with clear documentation, human approval gates, and measurable outcomes. ## What an AI-Driven Project Means - AI participates throughout the development lifecycle, including: - Specification writing - Code generation - Testing - Code review - Pull request creation and merging - Developers focus more on direction, judgment, and business decisions rather than repetitive implementation work. - The key methodology is **spec-driven development (SDD)**: - Requirements and implementation specifications are defined before code. - AI generates, tests, and reviews code against those specifications. - Structured specifications compensate for AI’s difficulty in interpreting ambiguous intent. ## Stage 1: AI-Ready — Establish Security and Compliance The first stage creates a safe foundation for using AI with project context and company data. - Remove hardcoded secrets such as API keys, database passwords, and internal IP addresses. - Use secret-management services to inject credentials dynamically at runtime. - Protect personally identifiable information by masking or tokenizing names, emails, phone numbers, and similar data before sending it to AI systems. - Separate or restrict access to critical intellectual property, including proprietary algorithms and sensitive architecture. - Define minimum compliance requirements first rather than delaying adoption until every security improvement is complete. - Use sandboxing, system prompts, filesystem restrictions, and network isolation to limit AI access. - Validate that isolation mechanisms actually prevent sensitive-data exposure. Expected benefits include safer AI usage, faster debugging and repetitive coding, and accumulated team experience that supports later adoption stages. ## Stage 2: AI-Assist — Standardize Team Usage This stage addresses teams where individuals already use AI but follow inconsistent practices. - Create project-level AI guidelines covering: - Project context - Coding conventions - Architecture principles - Domain terminology - Establish shared prompts, skills, or plugins for activities such as: - Brainstorming - Writing implementation plans - Code review - Subagent-driven development - Integrate AI into CI/CD for automated first-pass code reviews. - Let AI identify style violations, likely bugs, and security issues. - Reserve human review for complex business logic, architecture, and policy decisions. - At this stage, AI assists with human-written code rather than independently implementing features. Possible KPIs include: - A reduction in repetitive human review comments. - Increased test coverage. - Improved deployment reliability and system stability. - More consistent adherence to team conventions. ## Stage 3: AI-Development — Automate Implementation The third stage connects specifications directly to working code through an automated pipeline. - The pipeline includes three human approval gates: 1. **Specification review:** Confirm requirements, scope, edge cases, and validation criteria. 2. **Implementation and test-plan review:** Approve the AI-generated execution and testing plans. 3. **Code review:** Approve the final implementation before merging. - AI uses documented domain knowledge and architecture context to generate project-specific code. - A new file in a directory such as `/specs` can trigger CI automation. - CI can generate an implementation plan, execute coding tasks through independent subagents, run tests, and create a pull request. - Approval steps ensure that AI cannot proceed to the next stage without human authorization. To improve adoption, the post recommends expanding AI’s responsibilities gradually: - Begin with unit- and integration-test generation for existing logic. - Move progressively toward boilerplate and broader implementation work. - Avoid delegating critical business logic immediately, since poor early results can undermine team trust. ## Overall Adoption Principles - Each roadmap stage provides value independently; teams do not need to complete all four stages at once. - The appropriate target depends on team maturity, risk tolerance, domain complexity, and adoption speed. - Documentation is essential because AI needs structured project and business context. - Human oversight remains important, especially for requirements, architecture, business rules, and final code approval. - Security controls, common workflows, and measurable KPIs should develop alongside AI usage. Teams should start with the safest achievable stage, standardize practices before automating implementation, and expand AI’s role only as documentation, testing, and review processes become reliable.

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gitlab4 min readCurated summary

Introducing GitLab Orbit

GitLab Orbit is a public-beta, queryable graph that connects code with merge requests, pipelines, deployments, vulnerabilities, incidents, and ownership. GitLab argues that this unified context makes AI agents faster, more accurate, and capable of answering cross-system questions that traditional file search or RAG cannot. In testing, Orbit improved code-review accuracy while reducing agent runtime, token use, and hallucinations. ## The Problem with Code-Only Agents - Agents often struggle to understand the systems surrounding code: - Related files and dependencies - Tests and pipelines - Deployments and environments - Vulnerabilities and ownership - Work items and merge requests - In large or multi-repository codebases, agents can waste tokens exploring irrelevant paths, miss dependencies, or run out of context. - This can produce code that appears correct but is later reverted or requires substantial human correction. ## Results from Compare the Market - Compare the Market tested four context-retrieval approaches across 79 real merge requests. - An Orbit-grounded AI reviewer: - Placed accurate inline comments about 70% of the time. - Outperformed RAG, which achieved roughly 58%. - Produced slightly better summaries of key changes: 68% versus 66%. - RAG performed worse than the other tested approaches, including using no additional context. ## Faster and More Efficient Coding Agents - Claude Code and other agents can connect to Orbit through the Model Context Protocol (MCP). - Instead of crawling a repository to infer relationships, an agent can query the graph directly for: - Where code is located - What depends on it - Which tests and pipelines cover it - GitLab reports up to: - 11× faster execution - 4.5× lower token usage - 45× fewer hallucinations ## Cross-System Engineering Workflows Orbit enables agents on the GitLab Duo Agent Platform to investigate relationships beyond source code. - **Pipeline failure triage** - Agents can identify merge requests associated with a failing job. - They can find other projects and in-flight changes likely to encounter the same failure. - This can help teams resolve shared incidents centrally rather than repeating investigations. - **Vulnerability blast-radius analysis** - Queries can trace vulnerable components through services, pipelines, environments, and owning teams. - Security teams can produce assigned remediation plans shortly after a CVE is discovered. - **Engineering metrics** - Teams can query relationships between cycle time, pipeline failure rates, and deployment frequency without waiting for custom dashboards or SQL analysis. - **Migration planning** - Orbit identifies dependent services, jobs, environments, and owners. - This reduces the risk of discovering hidden dependencies late in a migration. ## Architecture and Access - Orbit ingests lifecycle data through change-data capture into ClickHouse. - It parses code in 12 languages, including Ruby, Java, Python, TypeScript, Rust, Go, C#, C++, and PHP. - GitLab reports indexing more than: - 40,000 projects - 500 million nodes - 2 billion edges - The graph can be queried through: - A Cypher-like query language - MCP - REST - The GitLab CLI - An event-driven engine keeps the graph current as changes occur. - Indexing runs separately from GitLab, so query traffic does not burden the GitLab instance. - Authorization follows existing GitLab permissions, limiting agents to data their users can access. - Queries pass through validation, planning, optimization, and security stages before reaching the database. ## Engineer-Facing Data Explorer - The Data Explorer provides direct access to the same graph without an AI agent. - Engineers can use it to: - Investigate incidents - Trace dependencies across services - Diagnose recurring CI failures - GitLab positions it as a way to answer open-ended system questions in seconds rather than reconstructing the answer manually across multiple tools. GitLab Orbit is best suited to organizations where code, CI/CD, security, and ownership data are spread across large repositories or many projects. Its main recommendation is to use one permission-aware graph as shared context for both AI agents and engineers, rather than relying on repository search or disconnected tool calls.

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gitlab3 min readCurated summary

GitLab Flex: Commit once, reshape your seats and AI spend

GitLab Flex addresses the unpredictability introduced by agentic software development, where seat counts, AI consumption, and desired capabilities can change throughout the year. Instead of fixing these needs in a traditional annual contract, Flex provides one annual spending commitment that can be reallocated monthly. GitLab’s conclusion is that organizations can adopt new capabilities and adjust usage without renegotiation or re-procurement. ## Fixed Contracts, Moving Needs - Agentic development creates uncertainty around: - The number of platform seats required as teams and contractor mixes change. - The amount of AI usage driven by evolving use cases and technology. - Which new capabilities organizations will adopt during the contract term. - Traditional contracts require customers to estimate all three in advance. - Overestimating leads to unused seats and capacity, while underestimating can delay adoption through additional procurement cycles. ## One Annual Commitment, Adjusted Monthly - GitLab Flex uses a single annual dollar commitment based on a published rate card. - Customers can allocate that commitment across: - Premium and Ultimate platform seats. - GitLab Credits for services such as Duo Agent Platform, hosted runners, and artifact management. - Eligible usage-based capabilities introduced after signing. - It applies across GitLab.com, Self-Managed, air-gapped, and Dedicated deployments. - Customers can shift unused seat reservations toward other seats or AI usage without amending the agreement. - Usage above the annual commitment is billed on demand at $1 per credit or the negotiated per-seat rate. ## Combined Seats, Credits, and Deployment Types - A single agreement can combine platform seats and credit-metered services. - Larger commitments provide volume discounts across the rate card. - Organizations can change their mix of seats, credits, and deployment models during the term. - Unlike models that separate licenses and usage credits, Flex allows budget to move between them. ## Pricing and Spending Controls - Reserved capacity is priced below unplanned usage. - Subscription-level and per-user caps help control spending. - Project- and group-level administrative controls provide additional oversight. - Unreserved seats use the same effective negotiated rate as reserved seats. - Cloud-connected customers are billed automatically; air-gapped customers are invoiced twice yearly. ## Existing Contracts and Availability - GitLab Premium and Ultimate remain available through direct seat pricing. - Existing customers may keep their current plans through renewal. - Flex does not change the capabilities included in those tiers. - Customers approaching renewal can compare Flex with their current contract using projected seat and AI usage. - Flex orders are available now, with fulfillment rolling out throughout the quarter. GitLab Flex is best suited to organizations that expect their workforce, AI consumption, or deployment requirements to change frequently. Its main benefit is financial and operational flexibility: one agreement lets customers rebalance spending monthly instead of waiting for renewal or reopening procurement.

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gitlab3 min readCurated summary

GitLab: Built for the agentic engineering era

GitLab argues that AI coding only becomes truly “agentic engineering” when paired with infrastructure built for machine-scale concurrency, lifecycle context, and enterprise governance. At GitLab Transcend, it announced new source control, context, security, orchestration, and purchasing capabilities designed to let agents work faster without sacrificing control. The overall goal is to convert rapid AI-generated development into reliable business value rather than unmanaged complexity. ## The challenge: speed without control - Research across more than 1,500 developers and technology leaders found: - 91% of organizations use at least two AI coding tools. - 54% use three or more. - Some customer codebases are growing by as much as five times per year. - Fragmented development lifecycles create several problems: - Human-scale source control systems struggle with thousands of concurrent agents. - Agents lack context about dependencies, deployments, and production behavior. - Rapidly changing code is difficult to govern. - Fixed contracts make AI adoption difficult to forecast. - 73% of respondents worry about maintaining AI-generated code, while only 21% see productivity improvements across the full SDLC. ## GitLab’s agentic infrastructure model GitLab presents its platform as four coordinated systems: - **Motor system:** Source control, pipelines, and deployments that execute work. - **Nervous system:** Context that helps agents and humans make informed decisions. - **Immune system:** Security, governance, identity, policy, audit, and approvals. - **Orchestration system:** GitLab Duo Agent Platform, which coordinates work across the lifecycle. The company says these systems operate consistently whether work is performed by a developer or an agent. ## Next-generation source control for agent concurrency Git’s traditional workflow creates bottlenecks when every developer runs hundreds of agents: - Agents repeatedly clone repositories even when they need only one file. - Thousands of simultaneous sessions can overwhelm a human-oriented backend. - Shared accounts and branches make it difficult to isolate, audit, or discard agent work. GitLab’s next-generation SCM, currently in private beta, retains Git protocol compatibility while redesigning the backend and interfaces for agents. It is intended to support thousands of parallel agents working safely across repositories. Early internal tests reported: - Up to 2× fewer tokens - Up to 50× faster wall-clock execution - Up to 1,000× less network traffic ## GitLab Orbit: lifecycle context for agents Agents often understand the code they modify but not the broader software lifecycle, causing wasted iterations, hallucinations, and incorrect work across large or multiple repositories. GitLab Orbit, in public beta, provides a continuously updated context graph connecting: - Code - Work items - Pipelines - Deployments - Production signals This gives agents and engineers a shared source of truth. GitLab reports that Orbit-grounded agents achieved up to 11× faster responses, 4.5× better cost efficiency, and 45× fewer hallucinations in early testing. Compare the Market’s testing on 79 merge requests found that graph-grounded agents placed inline review comments correctly 69.6% of the time, compared with 57.7% for a conventional RAG approach. ## Governance, orchestration, and purchasing GitLab also announced: - **Agents for security and governance for agents**, covering identity, policy, auditing, and approval of agent actions, in private beta. - **GitLab Duo Agent Platform**, generally available since January, allowing agents to pick up issues, review code, and fix pipelines. - **GitLab Flex**, a purchasing model intended to accommodate the unpredictable pace of AI adoption. - A Transcend hackathon inviting developers to build agents and workflows using Orbit. GitLab’s practical recommendation is not to slow down AI-assisted development, but to pair it with dedicated infrastructure for concurrency, full-lifecycle context, and enforceable governance.

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google3 min readCurated summary

New framework for auditing machine unlearning

Machine unlearning must be auditable, but conventional two-sample tests can be expensive, insensitive to localized changes, and prone to false positives. Google Research introduces Regularized f-Divergence Kernel Tests, which compare an unlearned model relative to both a safely retrained model and the original compromised model. The framework adaptively detects meaningful privacy or unlearning failures while controlling false positives and improving sensitivity as more samples become available. ## Why Standard Auditing Methods Fall Short - Auditors often lack access to model internals or the original training data, so they must assess unlearning by querying models and comparing outputs. - Standard two-sample tests require many samples to distinguish genuine violations from random variation in large models. - Maximum mean discrepancy (MMD) is effective for broad distribution shifts but can miss: - Rare, highly localized outliers - Non-smooth differences - Changes that require careful kernel-bandwidth tuning - Existing methods often require manual selection of statistics and hyperparameters. - Comparing an unlearned model only with a retrained reference can produce false positives because independently trained models may differ due to factors such as batch size. - Perfect retrain equivalence is generally impossible for local unlearning methods, since they retain traces of the original training process. ## Regularized f-Divergence Kernel Tests - The framework uses a relative-distance test to determine whether an unlearned model is closer to: - A safely retrained model that never saw the data - The original model that still contains the data - It supports multiple f-divergences for different types of distribution changes: - **Chi-squared and KL divergence:** Useful for smooth or localized differences, including outliers. - **Hockey-stick divergence:** Designed for privacy and unlearning thresholds, ignoring differences below a defined safety budget while flagging meaningful breaches. - Kernel regularization makes high-dimensional divergence estimation computationally tractable. - The adaptive procedure selects suitable divergences and hyperparameters automatically. - It avoids the need for sample splitting. - The authors theoretically show that false-positive rates are controlled for any sample size, while false-negative risk approaches zero as the number of samples increases. ## Experimental Evaluation - The framework was tested on: - Synthetic perturbed-uniform distributions - The Expo1D outlier-detection task from high-energy physics - High-energy physics provides a stringent benchmark because detecting rare particles requires identifying extremely subtle distributional differences. - For differential-privacy auditing, the researchers compared outputs generated from datasets differing by one record: - A private mechanism should produce statistically indistinguishable samples. - A non-private mechanism should reveal a detectable difference. - For machine-unlearning evaluation, the researchers used a three-sample relative test rather than simply comparing an unlearned model with a gold-standard retrained model. - The evaluation included methods such as Selective Synaptic Dampening, pruning, and random-label techniques. ## Practical Recommendation Auditors should use relative, divergence-based testing rather than relying on a single comparison with a retrained reference model. Regularized f-divergence kernel tests offer a more flexible way to detect localized privacy leaks and unlearning failures while reducing false alarms and costly manual tuning.

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discord2 min readCurated summary

Game On: Discord Is Backing the Next Generation of Dutch Gaming Founders

Discord and Techleap are launching the Gaming Founders Circle to support five ambitious Dutch gaming companies through the end of 2026. The program aims to help founders scale by connecting them with peers, investors, industry networks, and Discord leadership. It reflects Discord’s broader view that gaming grows through strong relationships among developers, players, and business communities. ## A New Support Program for Dutch Gaming - Discord is partnering with: - **Techleap**, which supports Dutch scaleups. - **The Dutch Games Association**, which provides connections to the local games industry. - The Netherlands is especially significant to Discord because it hosts the company’s European headquarters. - Discord says its platform offers developers direct access to players and real-time feedback: - More than 90% of Discord users play games. - Over 80 million users participate in more than 10,000 gaming communities. ## The Five Founders in the Cohort The first Gaming Founders Circle cohort represents a broad range of gaming technologies and business models: - **VaultN**: Digital distribution infrastructure used by publishers such as Bethesda, 2K, and Take-Two. - **Poki**: A self-funded web gaming platform with 90 million monthly players. - **MAXYMUM**: AI-powered tools for game design, recently validated at GDC. - **YOM**: Decentralized cloud gaming infrastructure combining blockchain and streaming. - **Immens**: A European game engine designed around AI and led by an experienced industry veteran. Discord, Techleap, and the Dutch Games Association selected the companies jointly. ## What Participants Receive The program focuses on practical challenges faced by growing gaming companies, including: - **Peer learning** on hiring, fundraising, distribution, community building, and international expansion. - **Investor access** through Techleap’s Dutch and international network, particularly important as later-stage studio funding becomes more difficult. - **Industry connections** through Discord’s gaming ecosystem. ## Program Milestones - The program runs through the end of **2026**. - In **October**, participants will travel to San Francisco for: - A meeting with Discord leadership. - An investor dinner hosted by Techleap and Prince Constantijn. - The initiative concludes at the **Dutch Games Awards in November**. The Gaming Founders Circle is intended to strengthen Dutch gaming by giving founders the relationships, knowledge, and capital connections needed to grow. Discord’s recommendation is clear: supporting individual companies while building stronger connections across the entire national games sector.

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gitlab2 min readCurated summary

GitLab on Google Cloud: Fully managed, compliant, and AI-ready

GitLab is introducing a fully managed deployment on Google Cloud through certified managed service providers such as Beyond and Digital Future. The offering combines data residency and compliance controls with access to Google’s Gemini and Gemma models through GitLab Duo Agent Platform. Organizations can also purchase the platform through Google Cloud Marketplace, applying existing cloud commitments to GitLab, AI inference, and infrastructure costs. ## Fully Managed GitLab on Google Cloud - Certified MSPs operate GitLab on Google Cloud under service-level agreements, removing infrastructure-management responsibilities from customer teams. - Organizations retain control over where code, pipelines, and security data are stored, supporting sovereignty and data-residency requirements. - GitLab’s audit and policy controls provide visibility into agent actions, merge requests, and security findings. ## AI Model Choice for Different Workloads - Gemini models, including Gemini 3.5 Flash, are available in Duo Agent Platform through Google’s Gemini Enterprise Agent Platform. - GitLab’s participation in Google’s early-access program is intended to bring new Gemini models to Duo as they become available. - Regulated or self-hosted teams can use Gemma 4 with GitLab Duo Self-Hosted. - With self-hosted models, the AI Gateway and all requests and responses remain within an organization’s on-premises or private-cloud environment. ## Using Existing Google Cloud Commitments - GitLab and Duo Agent Platform can be purchased through Google Cloud Marketplace. - Existing Google Cloud commitments can fund GitLab subscriptions, model inference, and related infrastructure without starting a new procurement cycle. - Consolidated Google Cloud billing reduces reconciliation across vendors. - GitLab retains its own cost-management features, including usage dashboards, model policies, and GitLab Credits for more predictable AI spending. ## One Governed DevSecOps Platform - GitLab Duo Agent Platform provides software-delivery context that standalone coding assistants lack, including merge requests, pipelines, and deployment targets. - This context helps agents perform multi-step work and supports code review at monorepo scale. - Combining GitLab’s governance and lifecycle data with Google’s models keeps deployment, model selection, compliance, and spending aligned in one platform rather than fragmented across multiple tools. Organizations can start with a Duo Agent Platform trial, enable it through the free GitLab tier, or use included GitLab Credits with Premium and Ultimate subscriptions. Overall, the offering is aimed at teams that want managed GitLab operations, flexible AI model access, and strong control over data location and costs on Google Cloud.

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figma2 min readCurated summary

Steal This Template: Bring a User Persona to Life with Figma Weave | Figma Blog

The post shows how Figma Weave can make Ideal Customer Profiles (ICPs) more vivid and useful by turning static personas into realistic visual scenes. Dropbox designer Sara Clayton used a headshot, prompts, and reference images to depict media professionals in authentic work environments. The result was faster iteration, greater emotional connection, and more effective storytelling for product strategy. ## Static Personas Lack Context - Dropbox’s ICPs represented video editors, audio engineers, and production managers. - Traditional diagrams, user stories, and repeated headshots failed to show these users in their real working environments. - Story-driven slide decks worked better, but lacked visual variety and realism. - Clayton wanted personas to feel more human and connected to tools such as timelines, mixing boards, and editing software. ## Creating Realistic Persona Scenes with Figma Weave - Clayton used Figma Weave to transform a profile picture into an image of a media producer working from home. - A single prompt placed the persona in front of a Premiere Pro screen. - She uploaded a reference image to revise the character’s outfit when the initial style was not appropriate. - Weave provided more control over visual variables and iterations than general-purpose chatbots such as Gemini or ChatGPT. - The workflow took less than two minutes. ## Figma Weave’s Role in AI-Native Creation - Figma Weave emerged from Figma’s acquisition of Weavy. - The platform combines generative AI with professional editing tools on an open canvas. - Its intended capabilities include image, video, animation, motion design, and VFX generation and editing. - The post also highlights persona-focused templates for car scenes, character sheets, outfits, and character variations. ## Impact on Product Strategy - More realistic visuals help teams connect with personas and understand their contexts. - Higher-fidelity storytelling can make ICPs more influential in strategic decisions. - Clayton’s broader goal is to “humanize” product work by showing users as real people rather than static profile images. - Readers can use the featured Figma Weave template to create a more engaging ICP quickly. Figma Weave is best suited for teams that want to supplement traditional persona documentation with realistic, editable visual narratives that make customer needs easier to understand and remember.

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discord2 min readCurated summary

Updated Requirements to How Apps Access Data in Servers

Discord is tightening how apps access message content, server member lists, and user presence data. Apps reaching 10,000 or more users must undergo review and renew approval annually, replacing the former threshold based on membership in 100 servers. Most users will notice no change, though apps that lose access may need to replace features such as text-command bots with slash commands. ## Updated Access Requirements - The changes affect access to: - Message content - Server member lists - User presence information, such as online or offline status - Apps serving fewer than 10,000 users are generally unaffected. - Apps reaching 10,000 or more users must complete Discord’s review process. - Approved apps must reapply every year to demonstrate that their data access remains necessary. ## Why Discord Is Changing the Rules - The previous review threshold was based on being installed in 100 or more servers. - Server count did not accurately reflect how many people an app could reach, especially when installed in one large community. - Annual reviews account for apps changing purpose over time. - Discord aims to ensure apps only retain access to data that is necessary, actively used, and consistent with their stated purpose. ## Impact on Developers and Users - Developers will receive notice and generally have 90 days to maintain access while applying or adapting their apps. - Developers may resubmit applications at any time. - Apps that lose access may continue working for features that do not depend on restricted data. - Text-based commands that require reading message content may need to be redesigned as slash commands. - Apps will not receive access to any new categories of data; the change concerns approval requirements and limitations. Discord recommends that developers review the updated requirements and that users inspect app profiles before deciding which apps to allow in their servers.

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aws3 min readCurated summary

Anthropic Claude Fable 5 on AWS: Mythos-class capabilities with built-in safeguards now available | Amazon Web Services

Claude Fable 5 is now available through Amazon Bedrock and Claude Platform on AWS, offering Mythos-level performance with safeguards for broader access. Anthropic highlights its ability to perform long-running tasks, analyze complex visual documents, and verify or improve its own work. Access requires specific data-sharing consent, and higher-risk requests may be routed to Claude Opus 4.8. ## Capabilities and Safeguards - Supports extended, asynchronous coding and knowledge-work tasks with minimal intervention. - Interprets diagrams, charts, tables, files, and PDFs for research, finance, legal, analytics, architecture, gaming, and software development. - Uses vision to compare implemented designs with intended goals. - Can update skills, create evaluation harnesses, and perform proactive self-verification. - Cybersecurity, biology, chemistry, and health prompts with elevated misuse risk may be handled by Opus 4.8 instead. - The unrestricted Claude Mythos 5 is limited to a small group of vetted customers. ## Accessing Fable 5 on Amazon Bedrock - Available through: - Anthropic’s Messages API using `bedrock-mantle` or `bedrock-runtime`. - AWS Invoke and Converse APIs through `bedrock-runtime`. - The Amazon Bedrock console Playground. - Model access is being expanded gradually across AWS accounts; customers can contact AWS Support for expedited access. ## Required Data Sharing - Users must opt into data sharing through the Data Retention API by setting `provider_data_share`. - No console interface is available for this setting at launch. - Anthropic requires: - 30-day retention of inputs and outputs. - Human review. - Data retention enables abuse detection across multiple interactions rather than isolated requests. - Example endpoints are provided for both `bedrock-mantle` and `bedrock-runtime`. ## SDK and API Usage - Install the Anthropic Python SDK with `pip install anthropic`. - The Messages API can be called through the Bedrock Mantle endpoint using model ID `anthropic.claude-fable-5`. - Boto3’s Converse API supports unified multi-model access through model ID `global.anthropic.claude-fable-5`. - Users can configure token limits and submit tasks such as designing a multi-region AWS architecture supporting 100,000 requests per second. ## Pricing and Routing - Requests routed to Opus 4.8 because of harmful content are charged at Opus rates. - If a conversation is blocked mid-request, initial tokens are charged at Fable rates and later tokens at Opus rates. - Pricing details are available on the Amazon Bedrock pricing page. Claude Fable 5 is best suited to ambitious, long-running workloads that benefit from advanced reasoning and document or visual understanding. Before using it, organizations should confirm account access, configure the required data-sharing settings, and evaluate whether the 30-day retention and human-review requirements fit their compliance policies.

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