u/OpenClawInstall

Anthropic launched Claude Design on April 17 as a collaborative design tool. Six days later, the open-source community has already shipped an alternative: Open-Codesign, and it supports more models than Anthropic's version.

What Is Open-Codesign?

Open-Codesign is an open-source, local-first desktop app that turns prompts into polished visual artifacts — prototypes, slides, PDFs, designs. It's positioned as an alternative to:

• Claude Design (Anthropic)
• v0 by Vercel
• Lovable
• Bolt.new
• Figma AI

The key difference: bring your own model and API key. Works with Claude, GPT, Gemini, Kimi, GLM, Ollama, and any OpenAI-compatible provider.

How It Compares to Claude Design

Feature	Claude Design	Open-Codesign
Models	Claude only	Claude, GPT, Gemini, Kimi, GLM, Ollama
Hosting	Cloud (Anthropic)	Local-first
Pricing	Included with Claude	Free (BYOK)
Data	Anthropic servers	Your machine
Export	Claude ecosystem	Standard formats
License	Proprietary	MIT

What It Does

• Prompt → Design — Describe what you want, get a polished prototype, slide deck, or one-pager

• Multi-model support — Import your Claude Code, Codex, or any OpenAI-compatible API key with one click

• Local-first — Your designs stay on your machine until you choose to share them

• Standard export — Export to standard formats, not locked into a specific ecosystem

• Desktop app — Electron-based, runs on macOS, Windows, and Linux

Why This Is Viral

The GitHub repo exploded in the first 24 hours because:

1. Timing — Launched 6 days after Claude Design, riding the Anthropic product wave
2. BYOK model — People who already pay for Claude/GPT/Gemini don't want another subscription
3. Multi-model — Claude Design only works with Claude. Open-Codesign works with everything.
4. MIT license — No commercial restrictions, fork it, modify it, ship it

The Open-Source Claude Alternative Stack

In one week, the community built alternatives to three Anthropic products:

• Open-Codesign → Alternative to Claude Design
• OpenWork → Alternative to Claude Cowork
• Open Cowork → Alternative to Claude Code desktop app

This is the fastest open-source response to a product launch I've ever seen. The community is essentially saying: "Nice product, Anthropic. We'll build an open version in a week."

Availability

• GitHub: OpenCoworkAI/open-codesign
• License: MIT
• Latest: v0.1.3 (April 21), v0.1.4 coming with image generation support
• Platforms: macOS, Windows, Linux
• Tags: claude-design-alternative, v0-alternative, bolt-alternative, lovable-alternative

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Sources: GitHub OpenCoworkAI/open-codesign, Anthropic

Anthropic launched Claude Cowork last week as a non-technical alternative to Claude Code. Within days, the open-source community shipped OpenWork — an open-source alternative that does the same thing but without the lock-in.

What Is OpenWork?

OpenWork is a desktop app (macOS, Linux) that wraps OpenCode into a team-friendly experience. Think Claude Cowork but:

• Local-first — Runs on your machine, no cloud dependency
• Bring your own model — Works with Claude, GPT, Gemini, DeepSeek, or local models
• Composable — Desktop app, Slack/Telegram connector, or server mode
• Ejectable — Powered by OpenCode, so everything works without the UI
• MIT licensed — Open source, no commercial restrictions

How It Compares to Claude Cowork

Feature	Claude Cowork	OpenWork
Model	Claude only	Any (BYOK)
Hosting	Cloud only	Local + cloud
Pricing	$25+/mo	Free
Data	Anthropic servers	Your machine
Integrations	Claude ecosystem	Slack, Telegram, any MCP tool
License	Proprietary	MIT

Key Features

• Orchestrator mode — Run OpenCode + OpenWork server without the desktop UI. Install via npm: npm install -g openwork-orchestrator

• Sessions — Create and manage multiple agent sessions with live streaming updates

• Execution plans — OpenCode todos rendered as a timeline, so you can see what the agent is doing and why

• Permissions — Surface permission requests and reply (allow once / always / deny). Critical for team environments.

• Templates — Save and re-run common workflows. Ship repeatable processes across your org.

• Skills manager — Import and manage .opencode/skills folders. Extensible without code changes.

• Debug exports — Copy or export runtime debug reports when something breaks.

Why This Matters

The AI agent tool market is splitting into two camps:

1. Closed ecosystems — Claude Cowork, Codex CLI, Cursor. Great UX, but locked to specific providers.
2. Open alternatives — OpenWork, Open-Codesign, Open Cowork. Less polished, but flexible and provider-agnostic.

For teams that need to:

• Use multiple AI providers (not just Anthropic)
• Keep data on-premise
• Customize agent workflows
• Avoid per-seat pricing

OpenWork is the clear choice.

The Agent Desktop War

Three open-source Claude alternatives launched in the same week:

• OpenWork — Team-focused agent orchestration (this one)
• Open-Codesign — Design-focused alternative to Claude Design
• Open Cowork — Desktop app wrapping Claude Code + others

The open-source community is systematically building alternatives to every Anthropic product. This is good for everyone — it keeps pricing competitive and prevents lock-in.

Availability

• GitHub: different-ai/openwork
• Website: openworklabs.com/download
• npm: npm install -g openwork-orchestrator
• macOS and Linux: Available now
• Windows: Coming (currently paid support plan)

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Sources: GitHub different-ai/openwork, openworklabs.com, Anthropic

Alibaba released Qwen3.6-27B on April 22 and it's already the most downloaded model on Hugging Face this week. A 27-billion-parameter dense model, Apache 2.0 licensed, with full weights available — and it outperforms Alibaba's own 397B MoE predecessor on every major agentic coding benchmark.

Let that sink in. A model 15x smaller beats the previous generation's flagship.

The Numbers

Benchmark	Qwen3.6-27B	Qwen3.5-397B (MoE)	Claude 4.5 Opus
SWE-bench Verified	77.2	76.2	80.9
Terminal-Bench 2.0	59.3	—	59.3
GPQA Diamond	87.8	—	87.0
SkillsBench Avg5	48.2	27.2	—

Read that Terminal-Bench row again. Qwen3.6-27B matches Claude 4.5 Opus exactly at 59.3%. A downloadable, open-weight, Apache 2.0 model matching Anthropic's closed frontier model on the benchmark that matters most for coding agents.

On GPQA Diamond, it actually beats Opus: 87.8 vs 87.0.

Why a 27B Dense Beats a 397B MoE

The architecture is doing the heavy lifting:

• Gated DeltaNet + Gated Attention — Each block starts with three Gated DeltaNet sublayers (linear attention, O(n) scaling) and caps with one Gated Attention layer. This matters for long codebases where quadratic attention runs out of memory.

• Asymmetric 24-query, 4-key/value attention — Smaller KV cache, lower VRAM at serve time. The model uses less memory while maintaining quality.

• Multi-Token Prediction — Trained to predict multiple tokens simultaneously, which speeds up inference without sacrificing accuracy.

• 64 layers — Deep architecture for a 27B model, enabling more complex reasoning chains.

Run It Locally

Here's the kicker: the 4-bit quantized version runs at 25.57 tokens/second on a 32GB MacBook Pro. No cloud API needed. No subscription. No rate limits. Just download the weights and run.

# GGUF version (llama.cpp)
pip install llama-cpp-python
# Or with SGLang for production serving:
python -m sglang.launch_server --model-path Qwen/Qwen3.6-27B --port 8000

What This Means for OpenClaw Users

If you're running OpenClaw with local models, Qwen3.6-27B is now the best option for coding tasks. It's:

• Better than DeepSeek V3 on coding benchmarks
• Matches Claude 4.5 Opus on Terminal-Bench
• Runs locally on consumer hardware
• Apache 2.0 (no commercial restrictions)
• Already has GGUF, MLX, and FP8 quantizations on Hugging Face

The Bigger Picture

This is the fourth Qwen3.6 release in three weeks. Alibaba is shipping open-source models at a pace that matches or exceeds the closed-source labs. The fact that a 27B dense model can match a closed frontier model on coding benchmarks means the open-source gap is closing faster than anyone expected.

The Hugging Face community is already celebrating — the model page has comments like "My deepest respect and admiration goes out to the Qwen team!!" and "I've been waiting for so long."

Availability

• Hugging Face: Qwen/Qwen3.6-27B (full weights), Qwen/Qwen3.6-27B-FP8 (FP8), unsloth/Qwen3.6-27B-GGUF (quantized)
• ModelScope: Available
• License: Apache 2.0
• Context: 262,144 tokens

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Sources: Hugging Face, GitHub QwenLM/Qwen3.6, Implicator.ai, BuildFastWithAI

Anthropic released Claude Opus 4.7 on April 16 and the official headline sounds great: same price, better model. $5/M input, $25/M output, unchanged from Opus 4.6. But if you're running Opus at scale, there's a catch buried in the release notes that could hit your wallet harder than any price increase.

The Tokenizer Trap

Opus 4.7 ships with a new tokenizer that produces up to 35% more tokens for the same input text. Same paragraph of English. Same Python function. Same JSON payload. Just... more tokens.

Anthropic didn't raise your rate card. They changed how your text gets counted.

Real-world impact: a request that cost $0.10 on Opus 4.6 could cost $0.10 to $0.135 on Opus 4.7, depending on your content mix. Code, structured data, and non-English text get hit hardest. And since output tokens are 5x more expensive than input, the verbosity increase compounds.

Before you migrate production traffic, replay real traffic side by side and measure the actual cost delta. Don't trust the "prices are unchanged" headline.

What Actually Improved

Pricing aside, Opus 4.7 is a meaningful upgrade:

• Agentic coding — The clearest win. Opus 4.7 handles complex, long-running coding tasks with more rigor and consistency. Users report being able to hand off their hardest coding work — the kind that previously needed close supervision — with confidence. It reads codebases, inspects multiple files, forms plans, uses tools, verifies outputs, and revises before finalizing.

• Vision upgrade — First Claude model with high-resolution image support. Ceiling raised from 1568px (1.15MP) to 2576px (3.75MP). Simpler 1:1 coordinate mapping. If you're doing screenshot analysis, diagram understanding, or document processing, this is a significant jump.

• Self-verification — Opus 4.7 checks its own work before reporting back. This is the same pattern we're seeing across the industry (GPT-5.5 does it too). The "plan → execute → verify → report" loop is becoming standard.

• Adaptive thinking — Automatically adjusts how much thinking it uses based on task complexity. Harder problems get more compute. Simpler ones respond fast.

• New xhigh effort level — Sits between high and max. Plus task_budget support for long-running agent loops. More granular control over the quality/speed tradeoff.

• Professional output — Anthropic says Opus 4.7 is "more tasteful and creative" on professional tasks. Higher-quality interfaces, slides, and docs. This matters if you're using Claude for client-facing work.

The Benchmarks

From Anthropic's model card and independent reviews:

• Opus 4.7 lags behind the unreleased Claude Mythos on every axis. Anthropic explicitly states: "Claude Opus 4.7 is less capable than Claude Mythos Preview on every relevant axis we measured." That's an unusual level of honesty from a model launch.

• On the GPT-5.5 comparison table we posted earlier, Opus 4.7 scored 69.4% on Terminal-Bench 2.0 (vs GPT-5.5's 82.7%) and 78.0% on OSWorld-Verified (vs GPT-5.5's 78.7%). Competitive but not leading.

• Where Opus 4.7 still wins: instruction following, long-context retrieval across 1M tokens, and the ability to sustain effort on multi-hour coding tasks without degrading.

Pricing Comparison

Model	Input ($/1M)	Output ($/1M)	Context
Claude Opus 4.7	$5	$25	1M tokens
Claude Opus 4.6	$5	$25	1M tokens
Claude Sonnet 4.6	$3	$15	1M tokens
Claude Haiku 4.5	$1	$5	200K tokens

Same sticker price. But with the new tokenizer, your effective cost per request goes up 0-35%. Prompt caching (90% discount) and batch processing (50% discount) remain the biggest levers for controlling cost.

Availability

• Claude Pro, Max, Team, and Enterprise users • Claude API (claude-opus-4-7) • Amazon Bedrock • Google Cloud Vertex AI • Microsoft Foundry • GitHub Copilot (rolling out now) • US-only inference available at 1.1x pricing

The Mythos Shadow

The elephant in the room: Anthropic released Opus 4.7 while simultaneously admitting it's not their best model. Claude Mythos — the model that leaked earlier this month — is more powerful on every benchmark. But Anthropic deemed it too dangerous for public release.

Opus 4.7 is what Anthropic considers safe enough to ship at scale. That's either reassuring (they're being responsible) or concerning (the gap between public and private models is growing).

What This Means for OpenClaw Users

If you're running OpenClaw with Claude as your backend:

1. Test before migrating — Replay your actual traffic against Opus 4.7 and measure cost. The tokenizer change is real.

2. Agentic coding is the killer feature — If your workflows involve multi-step coding tasks, Opus 4.7 is a genuine upgrade. The self-verification and sustained effort make it more reliable for autonomous work.

3. Vision workloads — The high-res image support is a significant jump if you're doing screenshot analysis or document processing.

4. Cost control — Prompt caching is your best friend. If you're sending similar system prompts across requests, the 90% discount on cache reads can offset the tokenizer overhead.

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Sources: anthropic.com, Mashable, CNBC, Finout, Evolink, GitHub Blog

Two years ago you couldn't get DALL-E 3 to spell "enchilada" on a restaurant menu without it inventing words like "enchuita" and "churiros." Those days are over.

OpenAI released ChatGPT Images 2.0 on Tuesday and it's the first image model with actual thinking capabilities. Not just diffusion from noise — it reasons through prompts, searches the web for real-time context, and double-checks its own output before showing it to you.

What's Actually New

• Thinking mode — This is the first image model with O-series reasoning. It can search the web mid-generation, create up to 8 images from a single prompt, and verify its own output. This isn't a diffusion model with better training data — it's a fundamentally different architecture.

• 2K resolution — Up to 2048px output in the API. Multiple aspect ratios supported. This is production-grade, not "good enough for social media."

• Text that actually works — Small text, dense layouts, iconography, UI elements. The things that broke every previous image model now render correctly. And not just English — significant gains in Japanese, Korean, Chinese, Hindi, and Bengali.

• Instruction following — You can describe a complex scene with specific object placement, relationships, and style constraints, and it actually follows through. Previous models would ignore half your prompt.

• Style fidelity — Photorealistic, manga, pixel art, watercolor, magazine design. The model captures defining characteristics of each aesthetic consistently.

Why This Matters for Agents and Automation

If you're building AI agents that need to generate visual assets — marketing materials, UI mockups, diagrams, social content — Images 2.0 changes the game:

1. Production-ready output — 2K resolution with correct text means you can go from prompt to deployable asset in one step. No more generating → fixing text in Photoshop → exporting.

2. Batch generation — 8 images from one prompt. Perfect for A/B testing creative, generating variations, or building asset libraries.

3. Web-aware generation — The model can search the web during generation. Ask it to create a poster about today's news and it'll pull current context.

4. API access — Available now via the gpt-image-2 API endpoint. If you're running OpenClaw with image generation skills, this is a drop-in upgrade.

The Benchmark Story

Images 2.0 hit #1 on Image Arena — the community benchmark where humans blind-rank image quality. It's not just OpenAI claiming it's better; independent evaluators put it at the top.

What It Replaces

This is the successor to DALL-E 3 / ChatGPT Images 1.5. The jump is significant enough that OpenAI isn't calling it an incremental update — they're framing it as "a new era of image generation." The text rendering alone is a generational leap.

The Competitive Landscape

• Midjourney V6 — Still strong for artistic/aesthetic work, but struggles with text and instruction following • Stable Diffusion 4 — Open source advantage, but Images 2.0 beats it on text rendering and reasoning • Gemini 3.1 Imagen — Google's entry, competitive on photorealism but lacks the thinking mode

Availability

• All ChatGPT users (free tier gets basic access, paid users get advanced output) • All Codex users • API: gpt-image-2 endpoint, available now • Knowledge cutoff: December 2025

What This Means Going Forward

The "AI images are easy to spot" era is ending. When a model can render a Mexican restaurant menu that could go straight to print without anyone noticing it's AI-generated, we've crossed a threshold. The next battleground isn't "can it make pretty pictures" — it's "can it do useful visual work at scale." Images 2.0 says yes.

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Sources: openai.com, TechCrunch, PetaPixel, MacRumors, CNET, 9to5Mac

OpenAI just released GPT-5.5 today and it's a significant step up from 5.4 — especially if you're using Codex for agentic coding. Here's what matters.

The Headline Numbers

GPT-5.5 vs GPT-5.4 vs Claude Opus 4.7:

Benchmark	GPT-5.5	GPT-5.4	Claude Opus 4.7
Terminal-Bench 2.0	82.7%	75.1%	69.4%
Expert-SWE (20hr tasks)	73.1%	68.5%	—
SWE-Bench Pro	58.6%	—	—
GDPval (wins/ties)	84.9%	83.0%	80.3%
OSWorld-Verified	78.7%	75.0%	78.0%
BrowseComp	84.4%	82.7%	79.3%
FrontierMath Tier 1–3	51.7%	47.6%	43.8%
FrontierMath Tier 4	35.4%	27.1%	22.9%
CyberGym	81.8%	79.0%	73.1%

The Terminal-Bench jump is the one that matters most for Codex users. That benchmark tests complex command-line workflows — planning, iteration, tool coordination — which is exactly what Codex does. Going from 75.1% to 82.7% in one release cycle is a massive leap.

What's Actually New

OpenAI describes GPT-5.5 as "a new class of intelligence for real work and powering agents." The key improvements:

• Agentic coding — GPT-5.5 reaches higher-quality outputs with fewer tokens and fewer retries. On Artificial Analysis's Coding Index, it delivers state-of-the-art intelligence at half the cost of competitive frontier coding models.

• Same latency — Larger models are usually slower. GPT-5.5 matches GPT-5.4 per-token latency in real-world serving while performing at a much higher intelligence level.

• Tool use and planning — Instead of carefully managing every step, you can give GPT-5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going.

• Strongest safety safeguards to date — Evaluated across OpenAI's full safety and preparedness frameworks, with nearly 200 trusted early-access partners providing feedback before release.

Codex CLI v0.124.0 Also Dropped

Alongside GPT-5.5, the Codex CLI got a major update:

• Quick reasoning controls — Alt+, lowers reasoning, Alt+. raises it. Accepted model upgrades now reset reasoning to the new model's default instead of carrying over stale settings.

• Multi-environment sessions — App-server sessions can now manage multiple environments and choose an environment and working directory per turn. Multi-workspace and remote setups are now first-class.

• Amazon Bedrock support — First-class Bedrock integration with AWS SigV4 signing and credential-based auth. If you're running Codex against Bedrock, this is native now.

• Stable hooks — Hooks are now stable, configurable inline in config.toml, and can observe MCP tools as well as apply_patch and long-running Bash sessions.

• Fast service tier default — Eligible ChatGPT plans now default to the Fast service tier unless you explicitly opt out.

What This Means for OpenClaw Users

If you're running OpenClaw with ChatGPT or Codex as your backend:

1. Upgrade your model config — GPT-5.5 is available right now in Codex. Update your model selection to gpt-5.5 and you should see immediate improvements on coding tasks.

2. Cost efficiency — Half the cost of competitive frontier models on the Coding Index means your agent runs cost less per task while producing better output.

3. Fewer retries — The model "checks its work" more effectively, which means fewer loop-and-retry cycles in your agent pipelines.

4. Bedrock users — If you've been routing through Bedrock, the native Codex support means cleaner auth and fewer configuration headaches.

The Bigger Picture

This is the third major model release from OpenAI in a week — ChatGPT Images 2.0 on Tuesday, Privacy Filter yesterday, and now GPT-5.5. They're clearly in a race with Anthropic (Claude Opus 4.7, Mythos Preview) and Google (Gemini 3.1 Pro) to lock down the enterprise and developer market before potential IPOs later this year.

The timing is notable too — the Musk v Altman trial starts Monday in Oakland. OpenAI is clearly trying to establish market momentum before that distraction dominates the news cycle.

Availability

• GPT-5.5: Rolling out today to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex • GPT-5.5 Pro: Rolling out to Pro, Business, and Enterprise users in ChatGPT • API: Coming soon (OpenAI says they're working with partners on safety and security requirements for serving at scale) • Codex CLI: v0.124.0 available now on GitHub

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Sources: openai.com, The Verge, 9to5Mac, CNBC, GitHub openai/codex

The Leak That Embarrassed Anthropic

In early April 2026, an unsecured database exposed Anthropic's most ambitious model yet. Claude Mythos — a multimodal AI system described internally as "by far the most powerful AI model we've ever developed" — sat exposed on the internet for a short window before Anthropic's security team caught it and pulled the plug.

The company confirmed it was real. They confirmed it was their most capable system ever. Then they pulled it citing "cybersecurity risks" — a phrase that's now the subject of more than a few Reddit threads and cybersecurity post-mortems.

What Claude Mythos Actually Was

Based on the documents and capabilities that circulated before the shutdown, Claude Mythos was not a simple iteration on Claude 3. It was an architectural step-change:

• Image generation — native image synthesis, not just captioning or understanding. Early testers described output quality competitive with Midjourney V6 at significantly higher resolution ceilings. • Code execution — full sandboxed runtime access, not just code completion. Mythos could execute, debug, and iterate on its own code in real-time with full environment inspection. • Extended reasoning — chain-of-thought depth that reportedly exceeded what had been possible in Claude 3.x by a significant margin. • Multimodal fusion — a unified architecture rather than bolted-together vision + language models.

Why Anthropic Pulled It

When a model this capable is exposed before launch, several things go wrong simultaneously:

• Model weights become reconstructable — any researcher who accessed the database during the exposure window has a copy of capability specifications • Safety eval gaps become public — the specific failure modes, adversarial prompts, and boundary conditions Anthropic found during safety testing are now known • Competitive timeline disrupted — the release schedule, feature set, and positioning against GPT-5 and Gemini Ultra all need to be rebuilt • Regulatory exposure — if the model was accessible externally in any form, it potentially violated compute cluster access agreements

The Community Response

The threads appeared immediately when the leak went public. One post on r/singularity hit 4.5K upvotes and 1K comments within 24 hours. The most discussed question: is this just marketing?

Counterpoint from the technical crowd: the benchmark numbers that leaked alongside the documentation were the actual story. Tests on reasoning tasks, code generation benchmarks, and multimodal evaluations all showed consistent improvements over Claude 3.7 Sonnet by margins that couldn't be faked or cherry-picked.

What This Means for the AI Arms Race

• Multimodal native architectures are the next battlefield • Code execution as a first-class capability changes software development forever • Safety vs. capability tension is accelerating at frontier labs • Leak risk is now a strategic variable every frontier lab must account for

Will Claude Mythos Ever Release?

Anthropic hasn't confirmed a timeline, but the pattern suggests a familiar arc: security audit, architecture review, infrastructure hardening, then a controlled release. The model is real. The capabilities are real. When it comes back, it will be the most watched release in AI history.

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Source: OpenClawInstall.AI/blog

Claude Mythos leaked briefly in early April before Anthropic pulled it citing "cybersecurity risks." Based on what slipped out, it was shaping up to be a multimodal monster — image gen, code execution, extended reasoning. Here's the post-mortem on what went wrong.

Google dropped Gemini 3.1 Pro in April with a reported GPQA Diamond score of 94.3% and 750M users. I tested both on LeetCode hards, React components, SQL queries, and bash scripting. One model clear won 7/10 tasks — and it wasn't the most expensive one.

GPT-5.4 Standard dropped March 5 alongside Thinking and Pro variants, but after two months of real use I'm underwhelmed. The price jump is real ($200/mo for Pro) and the reasoning gains over Claude Sonnet 4.6 are marginal for most tasks. Here's where it actually wins and where it doesn't.

Google dropped Gemini 3.1 Pro in April with a reported GPQA Diamond score of 94.3% and 750M users. I tested both on LeetCode hards, React components, SQL queries, and bash scripting. One model clear won 7/10 tasks — and it wasn't the most expensive one.

GPT-5.4 Standard dropped March 5 alongside Thinking and Pro variants, but after two months of real use I'm underwhelmed. The price jump is real ($200/mo for Pro) and the reasoning gains over Claude Sonnet 4.6 are marginal for most tasks. Here's where it actually wins and where it doesn't.

Google dropped Gemini 3.1 Pro in April with a reported GPQA Diamond score of 94.3% and 750M users. I tested both on LeetCode hards, React components, SQL queries, and bash scripting. One model clear won 7/10 tasks — and it wasn't the most expensive one.

GPT-5.4 Standard dropped March 5 alongside Thinking and Pro variants, but after two months of real use I'm underwhelmed. The price jump is real ($200/mo for Pro) and the reasoning gains over Claude Sonnet 4.6 are marginal for most tasks. Here's where it actually wins and where it doesn't.