DEV Community: Hamza

How to Replace Family Photo Texts With Google Photos Shared Albums

Hamza — Fri, 21 Aug 2026 05:43:53 +0000

Photo texting is a mess. One thread for your mom, another for your sister, duplicates piling up in every phone's camera roll. Google Photos shared albums solve this by giving your family one place to collect photos — no extra apps, no group chat chaos.

Here's how to set it up and why it beats group texts for sharing family moments.

Key Takeaways

Google Photos shared albums replace fragmented photo texting with one centralized location.
Create an album, invite family members, and choose who can add photos.
Partner Sharing offers automatic backup to one person's library.
Shared albums handle up to 10,000 items and keep comments for 365 days.

Why Shared Albums Beat Photo Texts

Texting photos scatters them across dozens of conversations. Everyone gets duplicate copies. You lose context when someone shares a photo weeks later, and storage piles up on every device.

Google Photos shared albums keep everything in one place. The album lives in the cloud. Anyone with access can view, add, or comment — no copies, no clutter.

The Sharing tab in Google Photos acts as a central feed. You see updates from all shared albums at once, so you never miss a moment.

Create Your First Shared Album

Start by opening Google Photos on your phone or computer. Tap the + button, then choose Album.

Select the photos you want to include. Swipe through your library or use the search bar to find specific images. Once selected, tap Add to album and give it a name like "Holiday 2024" or "Kids' Sports."

Now share it. Open the album, tap Share, and invite people. You can add anyone with a Google account using their email. Or create a link anyone with the link can view.

Choose who can edit the album. Only you can add photos means you stay in control. Allow others to add means everyone contributes — great for family trips where cousins each bring their own shots.

Different Ways to Share

Google Photos offers two main sharing approaches:

Shared Albums work like a group project. One person creates the album, then invites multiple people. Everyone can add photos (if enabled), comment, and see updates in real time. This is ideal for family reunions, kids' activities, or any situation where multiple people contribute.

Partner Sharing automatically backs up your photos to a shared album with one other person. You can share all photos, or filter by people or date range. When you take a new photo, it appears in both libraries. This is best for couples or parent-child pairs who want constant, automatic sharing without manual steps.

Both options keep privacy intact. Photos stay in your personal library unless you add them to a shared album. You can also remove photos from shared albums later if needed.

Keep Albums Organized

Name albums after events or themes. "Grandma's 80th Birthday," "Beach Trip July," or "New Puppy" are clearer than "Photos" or "More Photos."

Google Photos groups photos by people automatically. When you open a shared album, you'll see a Faces section showing everyone tagged in the photos. You can tag people yourself or let the algorithm do the work.

Create separate albums for different topics. One album for birthdays, another for holidays, a third for everyday moments. This makes it easy to find specific memories later.

Access and Save Photos

Anyone in a shared album sees updates in the Sharing tab of Google Photos. New photos appear instantly on all devices.

Recipients can save copies to their own library if they want offline access. This is optional — saved copies stay in their personal Google Photos, not in the shared album.

You can comment on photos in the album. Tag someone with @ to get their attention. Comments appear as notifications.

Limitations to Know

Shared albums have some constraints. Each album can hold up to 10,000 photos or videos. That's plenty for most families, but large collections might need multiple albums.

Albums store up to 365 days of comments. After that, comments disappear but photos remain.

Video resolution depends on your Google Account storage plan. Free accounts compress videos to 1080p. Paid plans preserve original quality.

Partner Sharing only works with one person. If you need to share with multiple people, use regular shared albums instead.

Get Started Today

Setting up a shared album takes five minutes. Start small — pick one family event or ongoing activity, invite a few people, and see how it feels. Most families find shared albums reduce photo clutter and make it easier to stay connected.

Once you see how smooth it is to share without texting, there's usually no going back.

Key Takeaways

Google Photos shared albums replace fragmented photo texting with one centralized location. Create an album, invite family members, and choose who can add photos. Partner Sharing offers automatic backup to one person's library. Shared albums handle up to 10,000 items and keep comments for 365 days.

Frequently Asked Questions

Do shared albums use storage space?
Yes. Photos you add count against your Google Account storage. But the shared album itself doesn't duplicate files — your original stays in your library unless you move it.

Can I share albums with people who don't have Google accounts?
No. All participants need a Google account to join a shared album. You can create a shareable link, but recipients still need a Google account to view or comment.

What happens if someone leaves the album?
When a member leaves, they lose access to future updates. Photos they added remain unless you remove them. You can also delete the entire album at any time.

Can I share albums across different devices?
Yes. Shared albums sync across all devices signed into the same Google account. View and manage albums on your phone, tablet, or computer.

How is this different from Facebook or Instagram albums?
Facebook and Instagram require social media accounts and expose photos to their platforms. Google Photos keeps everything private between people you invite. No algorithm decides who sees your family photos.

References

ReviewGeek/How-To Geek: I Stopped Texting Photos to My Family After Discovering Google Photos Album Sharing Feature
Google Blog: Shared Memories Made Easy With Google Photos
Google Photos Help: Create & Edit Photo Albums
Google For Families Help: Share Photos & Videos

Frequently Asked Questions

Do shared albums use storage space?

Yes. Photos you add count against your Google Account storage. But the shared album itself doesn't duplicate files — your original stays in your library unless you move it.

Can I share albums with people who don't have Google accounts?

No. All participants need a Google account to join a shared album. You can create a shareable link, but recipients still need a Google account to view or comment.

What happens if someone leaves the album?

When a member leaves, they lose access to future updates. Photos they added remain unless you remove them. You can also delete the entire album at any time.

Can I share albums across different devices?

Yes. Shared albums sync across all devices signed into the same Google account. View and manage albums on your phone, tablet, or computer.

How is this different from Facebook or Instagram albums?

Facebook and Instagram require social media accounts and expose photos to their platforms. Google Photos keeps everything private between people you invite. No algorithm decides who sees your family photos.

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References

SEC Proposes 'Regulation Crypto Assets' Framework for Crypto Investment Contracts (TekMag)

Hamza — Thu, 20 Aug 2026 21:27:43 +0000

The SEC proposed a new Regulation Crypto Assets framework that would create tailored exemptions and a conditional safe harbor for certain crypto investment contracts. The goal is clearer federal pathways for crypto offerings while preserving core investor protections.

Announced on August 18, 2026, the proposal represents a significant shift in how the SEC approaches crypto regulation under existing law. The framework builds on the Commission's March 2026 interpretive guidance and follows Chairman Paul S. Atkins' remarks at The Digital Chamber's Blockchain Summit earlier that day. For the official announcement, see the SEC press release.

The SEC also emphasized that these rules would preserve investor protections at the core of federal securities laws. Disclosure obligations and anti-fraud provisions would still apply even as certain offerings become easier to launch. That means issuers cannot rely on the exemptions to reduce transparency; rather, the framework aims to make required disclosures more predictable and principles-based.

Key Takeaways

Two new exemptions allow crypto offerings up to $5 million over four years and up to $75 million per year.
A conditional safe harbor removes crypto assets from investment-contract classification when issuer conditions are met.
State registration preemption reduces compliance friction for interstate crypto offerings.
The 60-day public comment period provides industry input opportunity.

Two Tailored Exemptions

The proposed rules include two exemptions from Securities Act of 1933 registration requirements specifically designed for crypto investment contracts. The first exemption permits offerings of up to $5 million during a four-year period with principles-based narrative disclosures. The second exemption allows offerings of up to $75 million during each 12-month period, requiring financial statements and ongoing reporting. This approach mirrors the structure seen in other securities exemptions while addressing crypto-specific characteristics.

The Conditional Safe Harbor

A key feature of the framework is the conditional safe harbor from the term "investment contract" in the Securities Act and Securities Exchange Act definitions of "security." If an issuer satisfies the proposed conditions, the crypto asset could be treated differently for securities-law purposes. This is intended to reduce regulatory uncertainty for projects that have completed or permanently ceased essential managerial efforts.

Preemption of State Requirements

The proposal would preempt state securities law registration and qualification requirements for offers and sales under the exemptions and for certain secondary market transactions. That could cut compliance complexity for issuers operating across multiple jurisdictions.

Why It Matters for Crypto Markets

The framework offers a potential middle path between full registration and regulatory ambiguity. It arrives as the CLARITY Act stalls in the Senate. At the same time, South Korea's stablecoin and crypto tax changes show how other jurisdictions are also rewriting digital-asset rules.

For founders and legal advisors, the practical impact may come down to how quickly they can structure compliant disclosures and decide whether to rely on the new safe harbor once it is finalized. Until then, most issuers should treat the proposal as a signal that the SEC wants clearer disclosure, not fewer rules, and plan their capital-raising timelines accordingly.

Comment Period and Next Steps

The public comment period will remain open for 60 days after publication in the Federal Register. Issuers, investors, and legal advisors can weigh in before the SEC finalizes or revises the framework. If adopted, the rules could change how U.S.-based crypto projects approach fundraising and secondary-market trading.

Frequently asked questions

What is Regulation Crypto Assets?

It is a proposed SEC framework creating tailored rules and exemptions for certain crypto investment contracts.

What are the offering exemptions?

One exemption covers up to $5 million over four years, and another covers up to $75 million per 12-month period with ongoing reporting requirements.

Does the proposal affect state securities laws?

Yes, it would preempt state registration and qualification requirements for covered offerings and certain secondary market transactions.

References

SEC Proposes 'Regulation Crypto Assets' Framework for Crypto Investment Contracts

Hamza — Thu, 20 Aug 2026 21:24:52 +0000

Key Takeaways

Two new exemptions allow crypto offerings up to $5 million over four years and up to $75 million per year.
A conditional safe harbor removes crypto assets from investment-contract classification when issuer conditions are met.
State registration preemption reduces compliance friction for interstate crypto offerings.
The 60-day public comment period provides industry input opportunity.

Two Tailored Exemptions

The Conditional Safe Harbor

Preemption of State Requirements

Why It Matters for Crypto Markets

Comment Period and Next Steps

Frequently asked questions

What is Regulation Crypto Assets?

It is a proposed SEC framework creating tailored rules and exemptions for certain crypto investment contracts.

What are the offering exemptions?

One exemption covers up to $5 million over four years, and another covers up to $75 million per 12-month period with ongoing reporting requirements.

Does the proposal affect state securities laws?

Yes, it would preempt state registration and qualification requirements for covered offerings and certain secondary market transactions.

References

Battletoads Returns on August 20 With Couch Co-Op and Dlala's Revival Formula

Hamza — Thu, 20 Aug 2026 13:37:41 +0000

Originally published at https://tekmag.thsite.top/battletoads-returns-on-august-20-with-couch-co-op-and-dlala-revival-formula/

Battletoads made its long-awaited return on August 20, 2020, when Dlala Studios and Rare shipped the first standalone entry in 26 years — a chaotic three-player couch co-op brawler that launched day-one on Xbox Game Pass across console and PC, with difficulty scaled from Tadpole to Battletoad.

The 2020 revival closed a gap that stretched back to the mid-1990s, bringing Rash, Zitz, and Pimple back in a side-scrolling beat-'em-up that mixed vehicles, bullet-hell arenas, and a heavy dose of the series' signature humor. This article breaks down what Dlala changed, why couch co-op was the centerpiece, and how the Game Pass strategy defined the launch.

Key Takeaways

Battletoads launched August 20, 2020 via Dlala Studios and Rare after a 26-year hiatus
Up to three players join locally with drop-in/drop-out co-op
Difficulty scales from "Tadpole" (casual) to "Battletoad" (brutal)
Day-one Xbox Game Pass release across console and PC
First standalone entry since 1994's Battletoads Arcade

What happened

Battletoads arrived on August 20, 2020 through Dlala Studios and Rare, ending a 26-year gap since the last mainline entry. The game launched on Xbox One, Windows 10, Steam, and Xbox Game Pass, bringing back Rash, Zitz, and Pimple in a side-scrolling beat-'em-up that mixed vehicle sections, bullet-hell moments, and puzzle-solving. Up to three players could join locally with drop-in/drop-out co-op, and difficulty ranged from "Tadpole" to "Battletoad."

The Dlala approach

Dlala Studios built the 2020 game around a specific philosophy: preserve the punishing difficulty that defined the original, but make it accessible through multiplayer and scalable difficulty settings. The studio kept the series' signature chaos intact while adding voice acting and an animated-story structure aimed at broader audiences. Developer interviews emphasized that the team grew up playing the NES versions and wanted to honor that legacy rather than soften it.

Couch co-op as the centerpiece

The three-player local co-op was the headline feature. Each player controlled one of the three toads with distinct animations and personality. The drop-in/drop-out system meant friends could join mid-run without restarting. Difficulty modes scaled from casual "Tadpole" for newcomers up to "Battletoad" for players seeking the series' traditional brutality. Vehicle sections and bullet-hell arenas broke up the standard beat-'em-up flow.

Platforms and Game Pass strategy

Battletoads launched day-one on Xbox Game Pass for both console and PC — the same subscription strategy that has remained central to Microsoft's Xbox studio plans since. The simultaneous release across Xbox One, Windows 10, and Steam was notable in 2020, offering broad accessibility without platform exclusivity. The Game Pass placement was central to the launch strategy, giving subscribers immediate access while building visibility for a franchise that had been dormant since 1994.

Story and voice cast

The narrative followed Rash, Zitz, and Pimple forming an uneasy alliance with the Queen against the Topian invaders. Full voice acting separated this entry from earlier entries that relied on text cards and sound effects. The animated-story packaging positioned the game as a more cinematic experience while retaining the series' irreverent humor and violent comedy.

Series context

The original Battletoads released on NES in 1991 and spawned sequels through the mid-1990s, including Battletoads & Double Dragon (1993) and Battletoads Arcade (1994), and you can find more of our gaming coverage here. The 2020 entry was the first standalone Battletoads game in 26 years. Rare, the original developer, provided support through Dlala Studios rather than leading development directly. The revival proved that franchises built around extreme difficulty and chaotic multiplayer could still find an audience decades later.

Key Takeaways

Battletoads launched August 20, 2020 via Dlala Studios and Rare after a 26-year hiatus
Up to three players join locally with drop-in/drop-out co-op
Difficulty scales from "Tadpole" (casual) to "Battletoad" (brutal)
Day-one Xbox Game Pass release across console and PC
First standalone entry since 1994's Battletoads Arcade

Conclusion

Battletoads' 2020 return worked because Dlala understood what made the original memorable: brutal, chaotic brawling that was always better with friends. By keeping the difficulty modes honest, making couch co-op the beating heart, and letting Game Pass put the game in front of millions of players on day one, the revival honored the NES original instead of sanding it down. It remains the best argument that single-screen co-op brawlers still have a place in a market dominated by live-service titles.

Frequently asked questions

Q: Is Battletoads a new 2026 release?

Is Battletoads a new 2026 release?

No. The August 20 date refers to the 2020 Dlala/Rare revival. No official 2026 Battletoads release has been announced.

Q: What platforms is Battletoads available on?

What platforms is Battletoads available on?

Xbox One, Windows 10, Steam, and Xbox Game Pass. It launched simultaneously across all four.

Q: How many players can play together?

How many players can play together?

Up to three players locally with drop-in/drop-out support.

Q: Who developed the 2020 Battletoads?

Who developed the 2020 Battletoads?

Dlala Studios led development with support from Rare, the original creator of the franchise.

Sources

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oMLX: The Open-Source Menu Bar LLM Inference Server Trending on GitHub

Hamza — Thu, 20 Aug 2026 09:26:17 +0000

Originally published at https://tekmag.thsite.top/omlx-the-open-source-menu-bar-llm-inference-server-trending-on-github/

oMLX is an open-source LLM inference server built for Apple Silicon that runs from your macOS menu bar, using tiered SSD caching and continuous batching to make local coding agents practical.

jundot/omlx recently hit 18.8k stars on GitHub, trending to #4 on GitHub Trending on May 10, 2026. It targets developers running Coding agents like Claude Code, Cursor, and OpenClaw locally on Mac.

Most local inference Tools cache KV state in RAM only. When a coding agent shifts context mid-session, the cache invalidates and the model recomputes from scratch. oMLX persists every cache block to SSD in safetensors format, so previously seen prefixes restore in milliseconds rather than seconds.

Key takeaways: oMLX runs local LLM inference from your macOS menu bar. It persists KV cache to SSD so coding agents don't recompute context on every shift. Continuous batching delivers up to 4.14x speedup on M3 Ultra 512GB. Supports OpenAI and Anthropic APIs with MCP tool calling. 18.8k GitHub stars and counting.

Menu bar control, web dashboard

The app lives in your menu bar. Start, stop, and monitor the server from there. A full web dashboard opens at http://localhost:8000/admin for model management, chat, benchmarks, and per-model settings. The dashboard supports English, Korean, Japanese, Chinese, French, Russian, Spanish, and Portuguese. All CDN dependencies are vendored, so everything works offline after the first run.

The macOS app is signed and notarized with in-app auto-update. It's not Electron. The Homebrew formula and source install are also available.

Continuous batching with measured speedups

oMLX uses mlx-lm's BatchGenerator for concurrent request handling. Benchmarks on an M3 Ultra 512GB show concrete gains:

With Qwen3-Coder-Next-8bit at 1024 prompt tokens and 128 generated tokens, 8x batching delivers 243.3 token/s, a 4.14x speedup over single-request throughput of 58.7 token/s.

Qwen3.5-122B-A10B-4bit on the same machine reaches 190.2 token/s at 8x concurrency, up from 56.6 token/s single-request. MiniMax-M2.5-8bit scales from 34.0 to 126.3 token/s. GLM-5-4bit moves from 16.7 to 60.3 token/s.

All measurements come from the official benchmark page. No cache reuse was applied during these tests.

Tiered KV cache architecture

The cache stack has two tiers. Hot blocks stay in RAM with write-back policy. Cold blocks move to SSD in safetensors format. The paged cache manager uses copy-on-write and prefix sharing across requests.

A ProcessMemoryEnforcer tracks total memory limits and TTL checks. An LRU eviction policy handles multi-model serving when RAM fills up.

This means long conversations with coding agents don't force full recomputation. The agent can circle back to earlier context and oMLX restores it from disk instead of re-running the prompt through the model.

Multi-model serving and API compatibility

oMLX serves LLM, vision-language, embedding, and reranker models simultaneously. It reads from the standard Hugging Face cache at ~/.cache/huggingface/hub, so models already downloaded by Transformers, MLX, vLLM, or llama.cpp work without re-downloading. It also picks up LM Studio folders and custom directories.

The API layer supports both OpenAI-compatible endpoints at /v1/chat/completions and native Anthropic endpoints at /v1/messages. Tool calling formats include JSON, Qwen, Gemma, GLM, and MiniMax. MCP integration is available with configurable tool result trimming for oversized outputs.

The admin dashboard generates the exact config Command for each supported client. Select a model, copy the command, paste into terminal. Works with Claude Code, OpenClaw, Codex, Cursor, and any OpenAI-compatible tool.

Install options and requirements

Requires macOS 15.0+ (Sequoia) and Apple Silicon (M1 through M4). 16GB RAM is the minimum, but 64GB+ is recommended for larger models. The sweet spot for daily coding work is an M-series Pro or Max with 64GB.

Three install paths:

macOS app: Download the DMG from Releases, drag to Applications. The welcome screen guides you through model directory, server start, and first download.

Homebrew: brew install omlx runs as a background service with auto-restart on crash. Logs go to $(brew --prefix)/var/log/omlx.log and ~/.omlx/logs/server.log.

Source: pip install -e . from the cloned repo. Building with native custom kernels for GLM-5.2 or MiniMax M3 requires full Xcode, not just Command Line Tools.

A plain pip install -e . does not build custom kernels. GLM-5.2 fused DSA prefill runs roughly 30x faster with kernels (845 vs ~29 tok/s on M3 Ultra). The fallback also uses more memory.

Why local Mac inference matters now

Coding agents invalidate KV cache frequently. Every context shift triggers full recomputation with most tools. oMLX changes that by persisting cache blocks to SSD across requests and server restarts.

The result is TTFT dropping from 30-90 seconds to under 5 seconds on long contexts, according to the project's benchmarks. A GitHub comment from a user running Qwen3.5 models describes the speed as making local AI on Mac "worthwhile," noting faster performance than LMStudio and more reliable tool calling.

The project started from vllm-mlx v0.1.0 and has evolved with multi-model serving, tiered KV caching, VLM support with full paged cache, an admin panel, and the macOS menu bar app. It uses Apple's MLX and mlx-lm, Blaizzy's mlx-vlm and mlx-embeddings, and dflash-mlx for block diffusion speculative decoding.

oMLX is Apache 2.0 licensed. The source is at github.com/jundot/omlx.

Conclusion

oMLX proves that local inference on Apple Silicon has moved past the hobby stage. Persisting KV cache to SSD fixes the biggest practical pain of running coding agents locally: context shifts no longer force full recomputation, and continuous batching turns a single M3 Ultra into a server that can keep up with concurrent requests. If you run Claude Code, Cursor, or any OpenAI-compatible tool on a Mac, the menu bar app is the lowest-friction way to test whether tiered SSD caching is worth swapping your current inference backend for.

Frequently asked questions

How is oMLX different from Ollama or LM Studio?

Ollama and LM Studio cache KV state in RAM only. When context shifts mid-session, the entire cache invalidates and recomputes from scratch. oMLX persists every cache block to SSD in safetensors format, so previously cached prefixes restore across requests and server restarts without recomputation.

What hardware do I need?

Apple Silicon (M1 or later) with macOS 15+. 16GB RAM is the minimum. 64GB+ is recommended for comfortable use with larger models. An M-series Pro or Max with 64GB is the sweet spot for daily coding work.

Does it work with Claude Code, Cursor, and other tools?

Yes. oMLX provides both OpenAI-compatible and Anthropic-compatible API endpoints. The web dashboard generates the exact config command for each supported client. It works as a drop-in backend for Claude Code, OpenClaw, Cursor, Codex, and any OpenAI-compatible tool.

Do I need to re-download models I already have?

No. oMLX reads the standard Hugging Face cache at ~/.cache/huggingface/hub, shared by Transformers, MLX, vLLM, and llama.cpp. It also picks up your LM Studio folder and custom directories. The admin dashboard includes a built-in HuggingFace downloader for new models.

What models are supported?

Any MLX-format model from HuggingFace. This includes Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, GLM, and more. Reasoning models get automatic <thinking> tag handling. Vision-Language Models are supported since v0.2.0 with the same paged SSD caching.

Sources:
jundot/omlx GitHub repository |
oMLX official site |
TrendShift repository insights |
Lobste.rs discussion |
daily.dev coverage

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NautilusTrader: The Open-Source Rust Trading Engine Powering Algorithmic Markets

Hamza — Wed, 19 Aug 2026 22:49:01 +0000

Originally published at https://tekmag.thsite.top/nautilustrader-the-open-source-rust-trading-engine-powering-algorithmic-markets/

Key Takeaways

NautilusTrader is a Rust-native trading engine with Python strategy bindings, offering deterministic event-driven backtesting and live trading under the same execution semantics.
The platform supports equities, futures, options, FX, crypto (CEX and DEX), betting exchanges, prediction markets, and tokenized real-world assets through modular adapters.
Version 1.228.0 (June 2026) added first-class DeFi support for BSC and Base chains, enabling on-chain pool replay alongside traditional exchange data.
The project holds SLSA Build Level 3 provenance and follows a bi-weekly release cadence with active community contributions on Discord and GitHub.

What Is NautilusTrader?

NautilusTrader is an open-source, production-grade trading engine built in Rust with Python strategy bindings, designed for multi-asset, multi-venue algorithmic trading from research through live execution. According to the project's GitHub repository, it spans research, deterministic simulation, and live trading within a single event-driven architecture, with the same execution semantics running across both backtest and production environments [1]. The engine was created by Nautech Systems Pty Ltd and has accumulated over 26,300 GitHub stars as of mid-2026.

Why NautilusTrader Exists

Algorithmic trading tools traditionally split into two camps: research-friendly backtesters like Zipline or Backtrader that lack production features, and institutional platforms that require expensive licenses and proprietary languages. NautilusTrader attempts to close this gap by offering a single engine that handles both research and live deployment without code changes. According to an analysis by Groundy, the platform emerged to address the bug-prone rewriting process that typically occurs when traders move strategies from research to production environments [3]. This research-to-live parity is the core value proposition that distinguishes NautilusTrader from alternatives in the open-source trading space.

How the Architecture Works

The platform uses a hybrid Python-Rust design where the core engine runs in Rust and Python serves as the control plane for strategy logic and orchestration. Critical performance paths including the matching engine, order book management, and serialization are implemented in Rust using PyO3 bindings, which replaced the earlier Cython layer in a migration that began in 2024. According to the official documentation, this architecture delivers sub-microsecond event processing for tick data and nanosecond-precision timestamps using Rust's time handling [2].

The event-driven message bus enables loose coupling between components. Strategies, data handlers, and execution modules communicate through typed messages rather than direct function calls. Messages fall into three categories: data (market ticks, bars, custom indicators), events (order fills, position changes), and commands (order submissions, cancellations). This classification ensures proper handling priority and enables sophisticated event processing pipelines, according to the project's documentation [3].

Asset Class and Exchange Coverage

NautilusTrader supports a broad range of asset classes through its adapter system. Equities, futures, options, and FX are handled natively alongside crypto perpetual contracts. The platform also supports betting markets through a Betfair adapter, prediction markets via Polymarket, and tokenized real-world assets. As of version 1.228.0, the integration roster includes Interactive Brokers, Binance, Bybit, Coinbase, Deribit, dYdX v4, Hyperliquid, Kraken, OKX, BitMEX, and data providers Databento and Tardis, according to the project's adapter documentation [1].

The June 2026 release marked a significant expansion with the addition of a blockchain adapter supporting BSC, Base, and associated decentralized exchanges including UniswapV3, PancakeSwapV3, and Aerodrome Slipstream. This means strategies can now route between centralized and decentralized venues using the same codebase, with backtest parity maintained by the shared matching engine [3]. The platform also supports a new analyze-pools CLI tool for batch DeFi pool snapshot hydration, enabling systematic pre-backtest data preparation.

Backtesting and Simulation

NautilusTrader's backtesting engine processes market data sequentially as discrete events rather than in vectorized batches, which eliminates lookahead bias, a common flaw in backtest systems where strategies inadvertently use future information. According to QuantStart's research on event-driven backtesting, this approach strictly enforces chronological processing so that strategies only access historical data available at each timestamp [4]. The Cache system maintains configurable history windows, defaulting to 10,000 bars per bar type and 10,000 ticks per instrument, providing strategies with efficient access to recent market data without loading entire datasets into memory [5].

Engine events can be captured into a durable log and replayed through the same execution path for research, audit, and debugging purposes. The platform supports multiple serialization formats including MessagePack for production use and JSON for debugging, with MessagePack providing 3-5x faster encoding and decoding than JSON according to the documentation [3]. Version 1.222.0 added Cap'n Proto serialization as an alternative, offering zero-copy deserialization for scenarios where data structures never need to be fully materialized in memory.

Performance and Production Deployment

High-frequency trading demands microsecond-level latencies, and NautilusTrader addresses this through Rust-implemented hot paths with sub-microsecond event processing, lock-free queues, and pre-allocated memory pools. While Python-based strategies cannot match pure C++ HFT systems, the platform enables medium-frequency strategies operating at microsecond timescales. According to Wikipedia's coverage of high-frequency trading, HFT's share of U.S. equity trading volume declined from its mid-2000s peak and accounted for roughly 10-40% of volume by 2016 [7].

For production deployment, NautilusTrader supports single-node, multi-process, and distributed configurations. The MessageBus integrates with Redis for cross-node communication in distributed setups. According to Interactive Brokers documentation, the platform's execution engine handles complex order lifecycle management including partial fills, amendments, and rejections that research tools often oversimplify [8]. The project also holds SLSA Build Level 3 provenance for supply chain security and publishes signed releases with vulnerability patching within 30 days of acknowledgment.

Development and Community

NautilusTrader is released under the GNU Lesser General Public License v3.0 and accepts contributions through GitHub with a required Contributor License Agreement. The project maintains a bi-weekly release schedule and hosts an active Discord community with over 5,800 members. According to the project's README, contributions should target the develop branch, and the project follows the Soundness Pledge, committing to be free of soundness bugs in its Rust implementation [1].

The project explicitly states that UI dashboards, distributed orchestration, and built-in AI/ML tooling are out of scope for the open-source version. These gaps are addressed through separate commercial offerings including NautilusTrader Pro and the NautilusTrader Cloud Platform, which provide managed infrastructure and institutional support. The open-source roadmap emphasizes stabilizing the Rust-native core, improving documentation, and enhancing code ergonomics for the v2 API transition.

For developers interested in building on the platform, the Python API provides access to the full scientific computing ecosystem through standard libraries like pandas and NumPy. Strategy development requires intermediate Python proficiency, while Rust knowledge is only necessary for modifying core platform components. The project also maintains community-contributed adapters for venues like Gate.io and Fyers, expanding exchange coverage beyond the official integration list.

Getting Started With NautilusTrader

Installation is straightforward through PyPI with pip install -U nautilus_trader, and prebuilt wheels are available for Linux (x86_64 and ARM64), macOS (ARM64), and Windows (x86_64) with Python versions 3.12 through 3.14. The project recommends using the uv package manager with vanilla CPython for the smoothest experience. Examples and documentation are available on the official site and GitHub repository, and the project maintains a detailed v2 migration guide for users upgrading from the earlier v1 release.

The platform's approach to algorithmic trading infrastructure, combining open-source accessibility with institutional-grade features, positions it as a notable option for quantitative researchers and trading developers. For comparison with other open-source development tools, you might also explore open-source video editing workflows or Rust-based database engines that share similar performance-first design philosophies.

Conclusion

NautilusTrader provides a production-grade, open-source trading engine that unifies research, backtesting, and live execution under a single event-driven architecture with Rust-level performance. Its active development cycle, broad asset class support, and growing DeFi integration make it a compelling option for algorithmic traders seeking institutional-grade capabilities without proprietary licensing.

Frequently Asked Questions

Q: What programming knowledge is required to use NautilusTrader?
A: Users need intermediate Python proficiency for strategy development. Rust knowledge is not required unless modifying core platform components. Familiarity with pandas, asynchronous programming, and financial markets is beneficial.

Q: How does NautilusTrader compare to commercial platforms like MetaTrader or NinjaTrader?
A: Unlike commercial platforms with scripting languages, NautilusTrader uses full Python with access to the entire scientific computing ecosystem. It offers greater flexibility for complex strategies but requires more setup than GUI-based platforms.

Q: Can NautilusTrader handle cryptocurrency trading?
A: Yes, the platform includes built-in adapters for major cryptocurrency exchanges including Binance, Bybit, Coinbase, Deribit, OKX, dYdX v4, Hyperliquid, and Kraken. Version 1.228.0 extended this to decentralized exchanges on BSC and Base via the new blockchain adapter.

Q: What are the hardware requirements for production deployment?
A: Minimum requirements are modest at 4 CPU cores and 8GB RAM for low-frequency strategies. High-frequency deployments benefit from dedicated servers with fast CPUs, kernel bypass networking, and co-location near exchange data centers.

Q: Is NautilusTrader suitable for retail traders?
A: While accessible to sophisticated retail traders, NautilusTrader targets professional and institutional users. The learning curve is steeper than consumer platforms, but the capabilities scale to institutional requirements without platform migration.

References

NautilusTrader GitHub Repository — Primary source confirming the project is an open-source, production-grade Rust-native trading engine with Python strategy bindings and event-driven architecture.
NautilusTrader Official Website — Confirms Rust-native core, nanosecond-resolution backtesting, live trading parity, multi-asset support, Docker availability, and SLSA Level 3 build provenance.
NautilusTrader: Building Production-Ready Algorithmic Trading Systems — Independent analysis covering hybrid Python-Rust architecture, sub-microsecond latency, event-driven vs vectorized backtesting distinction, and recent release history through mid-2026.
NautilusTrader: The Open-Source Trading Platform — Community explainer confirming high-performance positioning, multi-venue support, and backtesting-to-live deployment without code changes.
Event-Driven Backtesting with Python — QuantStart analysis explaining the advantages of event-driven over vectorized backtesting approaches for eliminating lookahead bias.
NautilusTrader Documentation - Cache — Official documentation describing the in-memory cache system with configurable history windows for bars and ticks.
High-frequency trading — Wikipedia entry providing historical context on HFT market share and latency requirements.
Interactive Brokers LLC — Broker documentation referenced for production deployment architecture and order lifecycle management details.

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Needle: The 14MB Open-Source Foundation Model for Tiny Devices

Hamza — Wed, 19 Aug 2026 16:49:18 +0000

Published on TekMag | Category: AI
Needle 2 is a 14MB open-source foundation model from Cactus Compute that runs tool calling, device control, and structured data extraction entirely offline on tiny hardware. At just 45 million parameters compressed to 2-bit precision, it fits in about 28MB of RAM and delivers 500+ tokens per second on a Raspberry Pi 5. The model reached #1 on GitHub Trending on August 14, 2026, after the repo gained nearly 5,000 stars in its first days.
Key Takeaways

- Needle 2 is a 45M-parameter, 14MB model optimized for tool calling and structured extraction

- It runs entirely offline using CQ2-bit quantization from Cactus Quants

- Auto-retrieval limits context to the top 5 tools when catalogs exceed five

- Confidence scoring prevents execution below a user-defined threshold

- Benchmark performance competes with models 5–70x its size on tool-calling tasks

- LoRA fine-tuning produces portable .cact files that run on the same engine

What Needle 2 Is
Needle 2 is a tiny language model built specifically for one job: calling tools and extracting structured data from text. It does not generate free-form prose. When a user request falls outside what the declared tools can handle, Needle returns an empty call and refuses to answer.
The model was designed by Cactus Compute and released under the MIT license on GitHub. The entire weights live in a single 14MB binary. Inference uses approximately 28MB of RAM. There are no separate model files to manage, and once the engine downloads from Hugging Face, it caches locally and runs completely offline.
How It Works
Needle uses what the authors call a Simple Attention Network. Instead of a standard feed-forward network, it applies a Hadamard transform (a fixed orthonormal matrix computed in O(n log n) time with no learned weights). Attention uses grouped-query attention with engram key-value memory. The architecture also includes multi-lane hyper-connections and gating mechanisms.
Every response is a function call. The model does not generate text between calls. Arguments come back as structured JSON constrained by a byte-level grammar compiled directly from your schema definitions. If a parameter is declared as a Literal, the grammar only admits those exact values. If a field has a numeric range, the model cannot emit a value outside it.
Tool retrieval kicks in automatically when you declare more than five tools. A built-in contrastive embedding head scores every tool against the current query and only the top five enter the context window. The grammar rebuilds over just that subset. Tool embeddings persist across sessions via a tool_index_path file keyed to a fingerprint of the schemas, so re-embedding only happens when the tool definitions change.
Memory and Confidence
Needle maintains a 256-token sliding KV window for conversation history. The tools themselves are pinned as permanent KV sinks, meaning they stay in memory regardless of how long the session runs. This is what keeps total memory near 28MB even during extended multi-turn interactions.
Every call carries a confidence score between 0 and 1. The score combines two signals: a calibrated post-hoc head that evaluates the full prompt plus the predicted call, and the decoding probability of the call tokens. Both signals must agree for the call to pass. Below your chosen threshold, the model escalates rather than executes a questionable call. The off-screen failure mode is refusal, not wrong execution.
Benchmarks
Needle 2 competes with models 5 to 70 times larger. Here is how it landed on the published benchmarks:

- Mobile Actions: Needle 2 scored 63.7%, compared to FunctionGemma 270M at 64.0% and LFM2.5 230M at 69.1%

- DroidCall: Needle 2 achieved 17.0%, versus FunctionGemma 270M at 17.5% and LFM2.5 230M at 11.0%

- Seal-Tools in-domain: Needle 2 reached 32.6% against LFM2.5 230M at 26.9% and FunctionGemma 270M at 16.3%

- Seal-Tools out-of-domain: Needle 2 posted 28.7%, while LFM2.5 230M hit 17.0% and FunctionGemma 270M hit 15.6%

- BFCL v4 single-turn overall: Needle 2 scored 42.6%, with FunctionGemma 270M at 46.1% and LFM2.5 230M at 60.8%

- Well-formed rate: Needle 2 maintained a 93.4% rate of structurally valid JSON outputs

The BFCL v4 gap reflects the benchmark's emphasis on complex multi-step reasoning. On narrower tool-calling tasks, Needle holds its ground against models an order of magnitude larger.
Installation and Usage
Install with pip:
pip install cactus-needle
The inference engine downloads once from Hugging Face and caches locally. No build step is required. You can review the official research background in the Simple Attention Network paper.
Here is a minimal example using the decorator API:
import needle
@needle.tool
def get_weather(city: str) -> dict:
"""Get the current weather for a city."""
return {"city": city, "temp_c": 27, "sky": "clear"}
agent = needle.Needle(tools=[get_weather])
result = agent.run("What's it like in Lagos right now?")
print(result["results"])

[{'city': 'Lagos', 'temp_c': 27, 'sky': 'clear'}]

For structured extraction, declare a Pydantic model and call extract():
from pydantic import BaseModel
class Invoice(BaseModel):
vendor: str
total: float
due_date: str
invoice = needle.extract("Invoice from Acme Corp, $1,200.00, due 2026-09-01", Invoice)
print(invoice.vendor, invoice.total)

-> Acme Corp 1200.0

Schema constraints use needle.Field with Annotated types. Supported validators include enum, const, ge/le/gt/lt, pattern, format, and length bounds. These compile directly into the decode grammar.
Fine-Tuning
Needle supports LoRA fine-tuning on the frozen base weights. The adapter merges cleanly at export, producing a single .cact file that runs on the same engine with no recompilation.
needle finetune data.jsonl --epochs 3 --generate 300 --lora-rank 16 --lora-alpha 32
needle build checkpoints/needle2.pkl --lora checkpoints/needle_lora.pkl --out my_needle.cact
Data format is JSONL. Each line contains a query, the tool schema, and the expected answer. The optional reasoning field lets you include the model's short derivation (e.g., 'kitchen' -> room; 'dim to 10' -> brightness 10), which improves fine-tuning quality without being grammar-constrained.
You can also synthesize training data from your tool schemas using an OpenRouter API key, which expands your examples before fine-tuning.
Where It Runs
Needle targets devices where larger models simply cannot fit:

- Raspberry Pi 5: 500+ tokens/sec decode

- VR headsets: 400–1,500 tokens/sec

- Phones under $200: 300–700 tokens/sec

- ESP32-class microcontrollers and other embedded platforms

Cactus Compute has shipped Needle in production inside the Pebble Index Ring, running locally within the Index 01 app. The model handles tool selection and structured responses without any network dependency.
Should You Use It?
Needle is not a general-purpose chat model. It will not write essays, summarize articles, or answer trivia. If your application needs to interpret natural language into tool calls, extract structured data from text, or control devices offline, it is purpose-built for that job.
The trade-off is size versus capability. At 45M parameters, Needle covers a narrow band of functionality exceptionally well. For applications that need broader reasoning alongside tool use, a larger model may be more appropriate. But for edge deployments, privacy-sensitive environments, or offline-first products, Needle removes the cloud dependency entirely.
Frequently Asked Questions
Q: Does Needle 2 require an internet connection?
A: Only for the initial engine download from Hugging Face. After caching, inference runs completely offline with no network calls.
Q: Can I use Needle for general text generation?
A: No. Needle is designed exclusively for tool calling and structured extraction. Off-topic prompts return an empty call rather than generating free text.
Q: What is the difference between Needle 1 and Needle 2?
A: Needle 2 uses the Simple Attention Network architecture with CQ2-bit quantization, significantly reducing model size while maintaining competitive benchmark performance compared to the original release.
Q: How does tool retrieval work with large tool catalogs?
A: When you declare more than five tools, a built-in contrastive embedding head scores each tool against the current query. Only the top five enter the context window, and the decode grammar constrains responses to that subset.
Q: Is Needle suitable for production use on microcontrollers?
A: Yes. Cactus Compute reports successful deployment on ESP32-class devices, and the model is already running in the Pebble Index Ring production app.
Conclusion
This article has examined the key developments, regulatory dynamics, and market implications of this topic. As the situation continues to evolve, stakeholders should monitor upcoming milestones and assess how these changes align with their strategic priorities.
References

- [1] cactus-compute/needle — GitHub repository (MIT license) — https://github.com/cactus-compute/needle

- [2] Needle 2 model weights — Hugging Face — https://huggingface.co/Cactus-Compute/needle2

- [3] Simple Attention Network paper (arXiv:2607.18363) — https://arxiv.org/abs/2607.18363

- [4] Cactus Compute product page — https://cactuscompute.com/needle

- [5] Coverage: MarkTechPost — https://www.marktechpost.com/2026/08/13/cactus-compute-needle-2-45m-parameter-tool-calling-model

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Comcast Turns Xfinity Gateways Into Motion Sensors With New WiFi Motion Feature

Hamza — Wed, 19 Aug 2026 06:00:03 +0000

Originally published at https://tekmag.thsite.top/comcast-turns-xfinity-gateways-into-motion-sensors-with-new-wifi-motion-feature/

Comcast is turning Xfinity home gateways into passive motion sensors with WiFi Motion, a new Xfinity Shield feature that infers movement from Wi-Fi signal behavior instead of cameras, wearables, or dedicated hardware.

Key Takeaways

WiFi Motion launched on August 18, 2026 as part of Xfinity Shield for compatible Xfinity gateways such as the XB7 and newer models.
The feature detects movement by measuring Wi-Fi signal disruptions between the gateway and stationary connected devices; it does not use cameras or collect images.
Users select up to three stationary Wi-Fi devices for motion sensing, and the Xfinity app pushes notifications when unexpected motion is detected.
Home Watch, Away Watch, and Dark Watch modes cover daytime, away-from-home, and overnight monitoring scenarios.
Comcast support language allows motion-related disclosures to third parties without further notice under law enforcement, dispute, or court-order circumstances.

A New Kind of Home Sensor

Comcast activated free Wi-Fi motion sensing on Xfinity gateways on August 18, 2026. The system works with XB7 and newer Xfinity Advanced Gateways and creates an oval-shaped detection zone between the gateway and a paired stationary device. That zone can cross rooms or floors depending on home layout and signal strength.

When motion occurs within that zone, the Xfinity app sends a push alert. Comcast says the feature does not capture video, audio, or still images, and it does not identify individuals. The company frames it as a convenience and safety layer built on existing broadband hardware, removing the need to buy separate motion sensors.

Home Watch, Away Watch, and Dark Watch

The Xfinity Shield app organizes motion detection into three modes. Home Watch monitors for unusual motion while occupants are present. Away Watch is meant for periods when the household appears empty. Dark Watch is tuned for overnight or low-light hours. Each mode changes the sensitivity and notification behavior, giving users a way to tailor the system to daily routines rather than treating every movement as an alert.

The Privacy Trade-off

WiFi Motion is opt-in and off by default. Enabling it still places motion data into Comcast’s notification workflow. Xfinity support language states that Comcast may disclose motion-related information without further notice for law enforcement investigations, legal disputes involving Comcast, or court orders and subpoenas. That disclosure scope is drawing attention from privacy watchdogs because it treats ISP-side ambient sensing data under broad third-party sharing language.

Privacy advocates note that motion patterns can reveal occupancy schedules, room usage, and household habits. Even without visual data, that metadata is valuable and sensitive. Comcast’s opt-in design gives users a choice, but the baseline disclosure terms set a low bar for how that data can flow once turned on.

Performance and Limitations

Detection quality depends on gateway placement, device placement, building materials, and Wi-Fi conditions. The system can detect human-scale and large-pet movement, but high sensitivity can also flag hand waves or other small motions. Some mobile devices and incompatible extenders are excluded from the sensing pool.

The technology is not guaranteed. Comcast warns that performance varies by home layout and signal strength, and that users may need to adjust sensitivity to reduce false positives. It also notes that the system does not professionally monitor alerts and is explicitly not a home security system.

Why It Matters

This launch mainstreams Wi-Fi sensing at ISP scale. Instead of requiring new sensors, Comcast is activating motion intelligence on routers already installed in millions of homes. For developers and security researchers, the notable story is not convenience alone; it is the normalization of ISP-side ambient sensing and the data-sharing terms attached to it.

Wi-Fi sensing has existed in niche products, but Comcast’s distribution reach changes the risk surface. The feature turns a connectivity appliance into a context-aware sensor, and the disclosure language places it under legal pathways that can share motion metadata without additional user consent.

Frequently asked questions

What is Xfinity WiFi Motion?

Xfinity WiFi Motion is a feature in Xfinity Shield that uses Wi-Fi signal disruptions between an Xfinity Gateway and stationary connected devices to infer motion without cameras or wearable sensors.

Which Xfinity gateways support WiFi Motion?

WiFi Motion is available for Xfinity Advanced Gateways such as the XB7 and newer models. It is included at no extra cost for eligible Xfinity Internet customers.

Does WiFi Motion capture video or audio?

No. Comcast states that WiFi Motion does not capture video, audio, or images, and it does not identify individuals. It infers motion from changes in Wi-Fi signal behavior.

Related Coverage

Sources: Comcast, The Verge, TechCrunch, Cybernews, Xfinity Support.

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OpenCut: The Open-Source CapCut Alternative That Just Hit 84,000 GitHub Stars

Hamza — Tue, 18 Aug 2026 22:44:13 +0000

Key takeaways

OpenCut is an MIT-licensed, open-source video editor positioned as a CapCut alternative, now sitting at over 84,000 GitHub stars and 8,300 forks.
The project is mid-rewrite: a Rust/WASM core powers GPU compositing and effects, while a Next.js frontend handles the web build and a GPUI-based client runs desktop.
Current releases ship as v0.1.0, v0.2.0, and v0.3.0, with an Editor API, plugin-first architecture, and an MCP server for AI agent automation all on the roadmap.
The classic version remains available at opencut.app while the rewrite progresses in parallel.
Local-first processing and an MIT license make it attractive for developers and creators who want to avoid CapCut’s licensing and data practices.

OpenCut is an open-source video editor building momentum on GitHub, drawing creators and developers away from CapCut with a model that keeps your footage local and your code public. The project, licensed under MIT, has crossed 84,000 stars and 8,300 forks since its first commit, signaling strong interest in a free alternative to one of the most popular consumer video editors on the market.

The rewrite under way pivots the project toward a Rust and WebAssembly core for GPU-accelerated compositing, masks, and effects. A Next.js frontend serves the web build, a GPUI-based client handles desktop, and the team plans a single codebase that ships to web, desktop, and mobile. The previous classic version still runs at opencut.app, while the new build cycles through v0.1.0, v0.2.0, and v0.3.0 with release notes published through April 2026.

Why OpenCut matters right now

CapCut dominates consumer video editing. It is free to download, packed with templates, and deeply integrated into TikTok’s creator ecosystem. But it also collects user data, locks certain features behind a Pro subscription, and ships without source code. OpenCut targets the gap left by that model. It offers the same kind of fast, template-friendly editing experience, but with a license that lets anyone inspect, modify, and redistribute the code.

The star count reflects more than curiosity. Crossing 84,000 GitHub stars places OpenCut among the most starred open-source creative tools of 2026. The fork count at 8,300 shows that developers are not just watching, they are building on top of it. Secondary coverage from AIToolly and The Menon Lab both flagged the project’s growing presence on GitHub Trending and its appeal to privacy-conscious creators.

Architecture: Rust core, GPUI desktop, Next.js web

OpenCut’s rewrite restructures the entire stack around a Rust and WASM backend. The core handles GPU compositing, mask rendering, and effects computation, offloading heavy work to the GPU while keeping the logic in a language known for memory safety. The web interface is a standard Next.js application, which means developers already familiar with the React ecosystem can extend or embed it. The desktop client uses GPUI, a Rust-native GUI framework designed for performance, and the team’s stated goal is a single codebase that targets web, desktop, and mobile without maintaining three separate repos.

This architecture matters for the developer angle. A Rust core with WASM bindings means plugins can run in the browser sandbox or as native extensions. The MCP server on the roadmap lets AI agents drive the editor programmatically, and the planned Editor API and scripting tab give users the ability to automate repetitive tasks, batch-export renders, or hook the editor into external pipelines. CoddyKit highlighted these hooks as the feature set that distinguishes OpenCut from a plain video editor: it is built to be extended.

What ships today and what comes next

Three versions are live so far: v0.1.0, v0.2.0, and v0.3.0. Release notes across those versions show a working timeline, basic effects, mask support, and export pipelines. The classic build remains at opencut.app for users who do not want to switch to the rewrite just yet.

The roadmap includes an Editor API that exposes the composition engine to external scripts, a plugin-first architecture with a package registry, an MCP server so AI agents can read and edit projects, headless mode for server-side rendering, and a scripting tab for running custom workflows inside the editor. Sponsors include fal.ai, which provides inference infrastructure for the AI-assisted features the team plans to add. Open-source tooling is moving fast in 2026: TekMag’s weekly Top 10 Trending GitHub Repos This Week and the GitHub’s Spec Kit, and developers are clearly looking for auditable, extensible toolchains rather than closed black boxes.

OpenCut already has 103 pull requests open or merged and 263 issues tracked. That volume suggests an active contributor base rather than a solo project with a large audience. The repo is private about specific release dates, but the cadence so far points to monthly minor releases with occasional larger feature drops.

OpenCut compared to CapCut

CapCut is proprietary, cloud-connected by default, and free for individual creators with a paid Pro tier for advanced features. OpenCut is MIT-licensed, runs locally, and charges nothing for anything. The feature overlap is real: both offer timeline editing, transitions, text overlays, and export to common formats. The difference shows up in control. With OpenCut, you can inspect the code, run it without an internet connection, strip telemetry, and build plugins that do exactly what you need. CapCut gives you templates and a polished interface, but it does not give you the code.

For teams that edit internally, distribute codebases, or handle sensitive content, the privacy model is the deciding factor. Footage never leaves the machine unless you choose to push it somewhere. That is not just a selling point, it is the architectural constraint baked into the design.

Who should try OpenCut

Creators tired of subscription tiers and data collection will find OpenCut worth testing. Developers interested in building video tools on top of an open editor will find the planned API and plugin system compelling. Teams that need to run headless renders in CI/CD pipelines will appreciate the headless mode once it ships. And anyone who prefers software they can audit before trusting with their work is the natural audience.

Conclusion

If you are used to CapCut’s template library and social-media-first workflow, there is a learning curve. OpenCut does not currently match CapCut’s polish on out-of-the-box templates, and the rewrite is still maturing. But for people who value transparency, local processing, and extensibility over curated presets, it is a meaningful shift.

Frequently asked questions

Is OpenCut completely free?

Yes. OpenCut is MIT-licensed and currently offers its core editing features without paid tiers or account requirements.

Does OpenCut work offline?

The local-first design keeps footage on your machine. Many operations, including timeline editing and rendering, can run without a constant internet connection.

Which platforms does OpenCut support?

The rewrite targets web, desktop, and mobile from a shared Rust core, with the classic version still available at opencut.app.

References

Craton Bolt — The Open-Source JIT-Compiled GPU SQL Engine That Compiles SQL to NVIDIA PTX at Runtime

Hamza — Tue, 18 Aug 2026 20:34:48 +0000

Craton Bolt is a SQL execution engine written in pure Rust that compiles each query into a fresh NVIDIA PTX kernel at runtime, loads it via the CUDA driver, and runs it on the GPU. There is no C++ shim, no precompiled kernel library, and no FFI to a third-party query engine. The full pipeline — parse, plan, codegen, launch — is Rust on top of the raw CUDA driver API.

The project comes from Craton, a nearshore engineering shop based in Buenos Aires, and sits at github.com/craton-co/craton-bolt under the Apache 2.0 license. It is currently at version 0.7.0 and is actively developed. The authors describe it as an experiment in whether a modern Rust crate can do what cuDF has done for years — except without dragging in a massive C++ dependency tree.

Key takeaways

Craton Bolt is a JIT-compiled GPU SQL engine written in pure Rust, targeting NVIDIA GPUs via the CUDA driver API.
It compiles each query into a single PTX kernel at runtime, fusing operations to avoid intermediate memory writes.
Arrow-aligned device buffers enable zero-copy PCIe transfers — the GPU sees the same memory layout as the CPU.
Compile-time borrow checking replaces runtime memory bugs with compiler errors, a shift from typical CUDA C++ development.
Version 0.7.0 supports a substantial SQL surface including joins, CTEs, window functions, and set operations, but GPU string kernels remain opt-in.
CI does not run GPU code; correctness is validated on developer hardware. Production use is not yet recommended.
The project is pre-1.0 with an unstable public API, aimed at Rust-native analytics workflows rather than replacing established GPU database systems.

Why another GPU SQL engine

The analytics GPU space already has players. NVIDIA's RAPIDS suite, and cuDF in particular, has been around since 2018. Sirius, a collaboration between the University of Wisconsin-Madison and NVIDIA, recently posted record ClickBench numbers. Both are solid. Both require CUDA-toolkit-level C++ builds.

Bolt takes a different path. It targets the same problem — accelerating analytical SQL on GPUs — but tries to eliminate the integration tax that comes with heavy C++ libraries. A data engineer who wants GPU acceleration should not need to manage CMake, fight ABI mismatches, or install 2 GB of CUDA dependencies just to run a GROUP BY faster. Bolt aims to be a cargo add away. If you are already using Polars or DataFusion, adding Bolt means adding another dependency and passing Arrow buffers across the PCIe boundary.

That is a narrower ambition than cuDF. It is also a narrower scope. Bolt does not claim to replace them. It claims to show that the core of GPU-accelerated SQL can live inside a single Rust crate.

How it compiles SQL to PTX

The pipeline has four stages.

First, sqlparser-rs turns the SQL string into an AST. Bolt then builds a logical plan and a physical plan — essentially a tree of operators that will execute the query.

Second, the JIT compiler walks that physical plan and emits a single PTX program. Each operator in the plan becomes a section of PTX. Because the entire expression tree is visible at codegen time, Bolt can fuse operations that would otherwise be separate kernel launches. This is the same idea that Polars and DataFusion use on the CPU side — fuse the computation, keep intermediates in registers, write to global memory only at the end.

Third, the PTX string is passed to the CUDA driver API. The driver JIT-compiles it into SASS for the actual GPU on the system. This happens at query runtime, not at crate build time.

Fourth, the kernel launches with Arrow-aligned GPU buffers as arguments. Results come back as Arrow arrays.

The result is a system where the query shape determines the GPU code shape. Two identical queries on different datasets produce identical PTX. Two different queries produce different PTX. There is no lookup table of prebuilt kernels. The PTX is generated on demand.

The memory model: CUDA-Oxide

Bolt's other distinguishing feature is how it handles GPU memory.

Most GPU dataframe libraries treat host memory and device memory as separate domains. You copy data from one to the other, often transforming the layout in the process. cuDF does this. BlazingSQL does this. The data travels across the PCIe bus in a proprietary device format that the GPU understands but the CPU does not.

Bolt keeps Arrow's memory layout intact on the device. It allocates GPU memory using a Rust type called GpuVec<T>. This type mirrors Arrow's columnar array layout — contiguous buffers, validity bitmaps, the works. When data moves from host to device, it is a bitwise copy across PCIe. No transformation. No re-alignment.

The borrow checker adds a safety layer on top. GPU memory is accessed through GpuView<'a, T> for reads and GpuViewMut<'a, T> for writes. These are exclusive, non-Copy handles. The compiler rejects code that would create simultaneous mutable and shared access to the same GPU buffer. Use-after-free, double-free, and aliasing bugs that slip through in C++ CUDA code are caught at compile time in Rust.

This matters because GPU memory bugs are notoriously hard to debug. A segfault on the host is loud. A segfault inside a kernel that ran 400 milliseconds ago is not.

What Bolt supports

As of version 0.7.0, Bolt covers a broad chunk of standard SQL.

The core operators are all there: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT. Joins include INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, and CROSS. Set operations cover UNION ALL, EXCEPT ALL, and INTERSECT ALL.

Advanced SQL features are supported too. CTEs work, including recursive CTEs with linear, non-linear, and mutual recursion. Derived tables and LATERAL subqueries in FROM are handled. Uncorrelated subqueries work, and there is support for a single correlated subquery in WHERE using EXISTS, NOT EXISTS, or scalar forms. VALUES acts as an inline row source. generate_series provides a table-valued function for generating sequences.

Aggregation gets the full treatment: ROLLUP, CUBE, and GROUPING SETS. Window functions run on the host with named WINDOW clauses and QUALIFY support. DISTINCT ON is available.

Scalar operations include IN, BETWEEN, CASE, CAST, COALESCE, NULLIF, and LIKE. Decimal128 has complete GPU arithmetic for addition, subtraction, multiplication, division, and comparisons, plus grouped SUM, MIN, and MAX. Date32 and Timestamp types support arithmetic including Date minus Date and Timestamp minus Timestamp with Day intervals.

String handling has two paths. Dictionary-encoded strings fold into integer membership predicates on the GPU — equality, inequality, IN, and LIKE become pure integer ops. The non-dictionary path, including LIKE matching and string transforms like UPPER, LOWER, CONCAT, SUBSTRING, and TRIM, runs on the host by default. GPU-accelerated string kernels exist behind an opt-in environment variable.

One notable gap: CI runs the full test suite using cuda-stub, which exercises zero GPU code paths. There is no GPU runner in CI. GPU correctness is validated on developer hardware. A green CI build means the host logic and codegen shapes are sound, not that the GPU kernels have been execution-tested in automation.

Performance and practical limits

Bolt does not publish benchmark numbers in its README. The project tracks them in docs/BENCHMARKS.md, but those are internal benchmarks run against specific hardware configurations, not independent third-party results. The project is pre-1.0, and the authors are clear about that in docs/LIMITATIONS.md.

The intended sweet spot is medium-sized analytical workloads — datasets that fit in GPU memory, queries that benefit from kernel fusion, and workloads where the PCIe transfer cost is amortized over significant computation. Small queries that spend more time moving data than computing it will not benefit. So far, the project targets sm_70 (Volta) and newer, requiring CUDA Toolkit 12 or later.

The comparison point is not cuDF or Sirius on raw throughput. It is integration cost. Bolt's value proposition is that a Rust project can add GPU acceleration without leaving the crate ecosystem. If you are already using Polars or DataFusion, adding Bolt means adding another dependency and passing Arrow buffers across the PCIe boundary. No new languages. No new build systems. No new abstractions to learn beyond SQL.

Where it fits in the stack

Bolt occupies a narrow lane. It is not a full database. It does not manage storage, transactions, or persistence. It is a query execution engine that takes Arrow data and SQL text and returns Arrow data. It is closest in philosophy to what DataFusion is to the CPU side of the modern data stack — a composable execution layer that plugs into existing tooling rather than replacing it.

The broader context matters. The GPU-accelerated analytics space is moving fast. Sirius recently set new ClickBench records. Starburst has been integrating cuDF into Trino. DuckDB is exploring GPU offload. The market is proving that GPU acceleration is viable for analytical workloads, but the integration story remains messy. Most solutions require significant infrastructure investment.

Bolt tries a different approach. Instead of building a complete GPU database, it builds a GPU execution engine that assumes the data is already in Arrow format and just needs to move across PCIe efficiently. The assumptions are narrower. The integration story is simpler. Whether that is enough to attract users beyond the Rust-native analytics community remains to be seen.

The idea behind Bolt is not new. JIT-compiling query plans into GPU kernels has been explored in academia for years. Projects like rNdN and various VLDB papers have demonstrated the performance potential. What is newer is the Rust angle — applying the same codegen approach inside a language that gives you memory safety and a manageable dependency graph instead of C++ and CMake.

Whether that tradeoff pays off depends on what you optimize for. If raw performance across every SQL operation is the goal, established projects with deeper CUDA expertise still lead. If the goal is reducing the friction of adding GPU acceleration to an existing Rust data stack, Bolt makes a case for itself.

The project is young. The API is unstable. Some features, like non-dictionary string operations on GPU, are still behind opt-in flags. But the architecture is sound, the code is open, and the approach — generate PTX at runtime, keep Arrow alignment on the device, let the borrow checker enforce memory safety — is a legitimate contribution to the GPU SQL conversation.

For anyone building data pipelines in Rust and looking for a way to push analytical queries onto a GPU without managing a C++ build chain, Bolt is worth watching.

Frequently asked questions

Is Craton Bolt a database?

No. It is a query execution engine. It takes SQL and Arrow data, runs the query on GPU, and returns Arrow data.

Do I need CUDA Toolkit installed?

Yes. Bolt requires CUDA Toolkit 12 or later and targets sm_70 and newer GPUs.

Can I use this in production?

The authors say no. The public API is unstable pre-1.0, and minor version bumps may break it.

How does this compare to cuDF or Sirius?

Bolt is narrower in scope. It aims to be a lighter-weight, Rust-native option for projects already in the Arrow/Rust ecosystem.

What SQL dialect does it support?

Bolt uses sqlparser-rs, which supports ANSI SQL with some extensions. The full supported surface is documented in docs/SQL_REFERENCE.md on GitHub.

References

GitHub repository: github.com/craton-co/craton-bolt
Craton blog post: craton.com.ar/blog/craton-bolt
Understanding PTX: developer.nvidia.com/blog/understanding-ptx-the-assembly-language-of-cuda-gpu-computing

CISA Flags Actively Exploited Ray Flaw That Can Trigger Browser-Based RCE

Hamza — Tue, 18 Aug 2026 18:14:13 +0000

CISA has added CVE-2025-62593, a critical code-injection flaw in the Ray AI compute engine, to its Known Exploited Vulnerabilities catalog after confirming active exploitation — and the attack chain can start from an ordinary web page in Firefox or Safari via DNS rebinding.

The vulnerability affects Ray versions before 2.52.0 and carries a CVSS 4.0 score of 9.4. The U.S. Cybersecurity and Infrastructure Security Agency added the flaw to its actively exploited KEV catalog on August 17, 2026, and federal agencies have until August 20 to remediate it under BOD 26-04.

Key Takeaways

CVE-2025-62593 is a critical (9.4) code-injection flaw in Ray, the open-source distributed AI compute engine, affecting versions before 2.52.0.
The attack is browser-based: DNS rebinding turns Firefox or Safari into a relay to a local Ray dashboard, bypassing weak User-Agent defenses.
CISA added it to its KEV catalog on August 17, 2026 after evidence of active exploitation; federal remediation deadline is August 20.
Fix: upgrade to Ray 2.52.0 or later, which patches the flaw and adds a disabled-by-default token authentication option.

What Is CVE-2025-62593?

Ray is a Python-native distributed computing framework used to scale AI and machine learning workloads, with more than 43,500 GitHub stars. The flaw is rooted in a longstanding design decision: the Ray dashboard exposes critical endpoints such as /api/jobs and /api/job_agent/jobs/ without authentication. According to the GitHub security advisory published in November 2025, this "has once again led to a severe vulnerability that allows attackers to execute arbitrary code against Ray." The NVD entry classifies it under CWE-94 (code injection) and CWE-352 (cross-site request forgery).

How the Browser-Based Attack Works

The interesting part is the attack surface: it is not limited to network-adjacent attackers. Ray's dashboard tried to block browser traffic by checking whether the request's User-Agent header starts with "Mozilla". That heuristic is insufficient, because the fetch specification allows web pages to set a custom User-Agent — and Firefox and Safari permit this, while Chrome happens to block it due to a browser bug.

Researchers combined that gap with a DNS rebinding attack. If a developer running Ray visits a malicious site or is served a malicious ad, the site can rebind its domain to 127.0.0.1 and submit requests to the local Ray dashboard as if they came from the browser's own origin, executing arbitrary shell commands. The public PoC uses NCC Group's Singularity framework and can also target Ray instances inside a private corporate network by using the browser as a confused-deputy intermediary.

Why It Matters for AI and Developer Workflows

Ray is the orchestration layer behind many training and inference pipelines, so a compromise can expose model code, datasets, and internal tooling. This is not just a hypothetical risk: The Hacker News reported that the RondoDox DDoS botnet had already incorporated the vulnerability into its arsenal two days before public disclosure, and unpatched Ray instances have been used to turn NVIDIA GPU clusters into a self-replicating cryptocurrency mining botnet in the ShadowRay 2.0 campaign. If you are building AI coding workflows on local services, this is a concrete reminder that local tooling is an attack surface; the same goes for CI workflow exposure via coding agents.

What to Do Now

Upgrade Ray to 2.52.0 or later immediately — the patched release also adds a disabled-by-default token authentication option for the dashboard.
Review local and network-adjacent Ray deployments for unnecessary exposure, especially ports such as 8265 and 6379.
Monitor the KEV catalog and vendor advisories for follow-on guidance, the same pattern as the recently exploited CISA SharePoint flaw.
Assume compromise if Ray was reachable from a browser-connected network: audit running jobs, shell history, and GPU/CPU utilization for mining workloads.

Conclusion

CISA’s KEV listing and the August 20 federal remediation deadline make this a near-term action item for any team running Ray. The browser-based trigger lowers the barrier to exploitation enough that even local development instances count as exposed — upgrade to 2.52.0, enable token authentication if you cannot upgrade immediately, and treat the flaw as a critical patch priority rather than a routine update. Are you running any Ray versions older than 2.52.0? Let us know in the comments what your upgrade plan looks like.

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Frequently asked questions

What is CVE-2025-62593?

It is a critical (CVSS 9.4) code-injection vulnerability in Ray, the open-source distributed AI compute engine, affecting versions before 2.52.0. It allows remote code execution via browser-based attacks involving DNS rebinding.

Which Ray versions are affected?

All versions before 2.52.0 are affected. The flaw was patched in version 2.52.0, which also introduced a disabled-by-default token authentication feature for the dashboard.

Why does the attack involve Firefox and Safari?

Ray's dashboard blocked requests whose User-Agent header starts with "Mozilla". Firefox and Safari allow web pages to set a custom User-Agent via the fetch API, so the block can be bypassed. Chrome currently blocks that behavior due to a browser bug.

How do I fix the Ray vulnerability?

Upgrade Ray to version 2.52.0 or later. Also review local and network-adjacent Ray deployments for unnecessary exposure, and consider enabling token authentication if the dashboard is reachable from a browser-connected network.

Sources: CISA KEV Catalog Alert, NVD CVE-2025-62593, Ray GitHub Security Advisory, The Hacker News

Higgsfield Raises $400M Series B at $5.4B Valuation to Scale AI Video and Image Creation Platform

Hamza — Tue, 18 Aug 2026 05:18:50 +0000

Higgsfield Raises $400 Million at $5.4 Billion Valuation

Higgsfield, the San Francisco-based AI video and image creation platform, announced on August 17, 2026 that it has raised $400 million in Series B financing at a $5.4 billion valuation. The round more than quadruples the company's $1.3 billion valuation from January 2026.

The round was led by DST Global, with participation from Growth Equity at Goldman Sachs Alternatives, Tribe Capital, Smash Capital, Fifth Wall, Valor Capital, Intel Capital, Liberty Global Tech Ventures, Mirae Asset Capital, and NTT DOCOMO Ventures. Existing investors Accel, Menlo Ventures, AI Capital Partners, GFT Ventures, Capra Ventures, BAM Corner Point, and BroadLight Capital also participated.

Natalia Vodianova Arnault joined as both investor and advisor.

Revenue Tripling in Six Months

Annualized revenue hit $700 million in August 2026, up from roughly $20 million a year earlier. The company reports over 30 million users across 238 countries and territories.

Enterprise adoption is now the majority of revenue. Higgsfield powers visual production for 390 Fortune 500 companies, a dramatic shift from less than 25% of revenue coming from business customers in January 2026.

The growth came partly from agentic AI products that launched in May 2026. After the Supercomputer rollout, adoption grew 42-fold in three months. The platform now processes more than 20 million content generations per month.

What the Money Will Fund

Higgsfield says the funds will support research and development, global infrastructure, elite AI talent recruitment, and scaling go-to-market efforts worldwide.

The company is also investing in education and philanthropy. Higgsfield Academy offers free AI video training and has attracted over 400,000 visitors with 67,000 lesson completions. Higgsfield For Good launches in September 2026 to help schools and nonprofits localize visual learning materials.

Key Players

Co-founders include Alex Mashrabov (CEO), Yerzat Dulat (CTO), and Mahi de Silva. The board now includes a representative from DST Global following the investment.

Why This Round Matters

The valuation jump signals growing investor confidence in AI video. Dealroom notes the round ranks in the top 1% of late-stage tech rounds in its market all-time. The speed of the enterprise pivot from consumer tool to Fortune 500 infrastructure mirrors a broader shift across the generative AI industry. AI infrastructure spending is also accelerating in adjacent areas such as watermarking and agent tooling.

How Anthropic Is Watermarking Claude AI Text

Snowflake GitHub Actions Flaw Lets Crafted Issues Trigger Command Injection

Zoomsday: How AI Helped Build a Critical Zero-Click Zoom Exploit in One Day

Key Takeaways

$400M Series B raised at $5.4B valuation

$700M annualized revenue, tripled in one year

390 Fortune 500 companies now using the platform

30M+ users across 238 countries

Lead investor: DST Global

What's Next

Higgsfield plans to use the capital to accelerate product development and expand globally. The company will continue building its agentic AI tools and scale its enterprise offerings.

References

This article was originally published on TekMag. Read the full story there.