@TryMorphiq

AI Lab shaping coding agent experience.

 San Francisco, California
trymorphiq.com/
Joined May 2025
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  • Followers93
Morphiq Labs retweeted
Ashish@itsashonarollSep 11

From Developer Preference to Agent-Led Growth

Article · @itsashonaroll
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Morphiq Labs retweeted
Aryan@whichmantechSep 9
How efficiently can coding agents use a sandbox? Our benchmark using public docs, ranked by MAXI: 1. @modal 81.8 2. @daytonaio 77.5 3. @e2b 74.2 4. @RunloopAI 67 5. @vercel Sandbox 61.1 MAXI = agent cost, time & friction. Full results below.
Morphiq developer sandboxes benchmark, all tasks and coding-agent models, verified September 8, 2026. Screenshot of the public website leaderboard, with MAXI and task success: Modal: 81.8 MAXI, 96.7% success; Daytona: 77.5 MAXI, 88.3% success; E2B: 74.2 MAXI, 81.7% success; Runloop: 67 MAXI, 83.3% success; Vercel Sandbox: 61.1 MAXI, 85% success. MAXI combines agent cost, task completion time and validated friction; success is separate. Documentation-based evaluation, not production runtime performance.

ALT Morphiq developer sandboxes benchmark, all tasks and coding-agent models, verified September 8, 2026. Screenshot of the public website leaderboard, with MAXI and task success: Modal: 81.8 MAXI, 96.7% success; Daytona: 77.5 MAXI, 88.3% success; E2B: 74.2 MAXI, 81.7% success; Runloop: 67 MAXI, 83.3% success; Vercel Sandbox: 61.1 MAXI, 85% success. MAXI combines agent cost, task completion time and validated friction; success is separate. Documentation-based evaluation, not production runtime performance.

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Morphiq Labs retweeted
Aryan@whichmantechSep 9
How efficiently can coding agents use AI gateways? Our benchmark using public docs, ranked by MAXI: 1. @openrouter 74.3 2. @portkeyai 66.5 3. @braintrust 38.1 4. @vercel AI Gateway 22.8 5. @litellm 20.8 MAXI = agent cost, time & friction. Full results below.
Morphiq ai gateways benchmark, all tasks and coding-agent models, verified September 8, 2026. Screenshot of the public website leaderboard, with MAXI and task success: OpenRouter: 74.3 MAXI, 73.3% success; Portkey: 66.5 MAXI, 88.3% success; Braintrust Gateway: 38.1 MAXI, 61.7% success; Vercel AI Gateway: 22.8 MAXI, 75% success; LiteLLM: 20.8 MAXI, 83.3% success. MAXI combines agent cost, task completion time and validated friction; success is separate. Documentation-based evaluation, not production runtime performance.

ALT Morphiq ai gateways benchmark, all tasks and coding-agent models, verified September 8, 2026. Screenshot of the public website leaderboard, with MAXI and task success: OpenRouter: 74.3 MAXI, 73.3% success; Portkey: 66.5 MAXI, 88.3% success; Braintrust Gateway: 38.1 MAXI, 61.7% success; Vercel AI Gateway: 22.8 MAXI, 75% success; LiteLLM: 20.8 MAXI, 83.3% success. MAXI combines agent cost, task completion time and validated friction; success is separate. Documentation-based evaluation, not production runtime performance.

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Morphiq Labs retweeted
Aryan@whichmantechSep 10
How easily can coding agents use web data tools? 10 common tasks, public docs. MAXI ranking: 1. @firecrawl 76.9 2. Bright Data 71.8 3. @zytedata 64.6 4. @apify 63.5 5. @Oxylabs_io 62.3 MAXI = agent cost, time & friction. Full results below.
Morphiq web-data-extraction benchmark, all ten tasks and all coding-agent models, verified September 10, 2026. Firecrawl: 76.9 MAXI, 90% task success, 8.3% validated friction; Bright Data: 71.8 MAXI, 86.7% task success, 3.3% validated friction; Zyte: 64.6 MAXI, 78.3% task success, 0% validated friction; Apify: 63.5 MAXI, 83.3% task success, 0% validated friction; Oxylabs: 62.3 MAXI, 78.3% task success, 6.7% validated friction. MAXI combines agent cost, completion time and validated friction. Task success is separate. Public-docs evaluation.

ALT Morphiq web-data-extraction benchmark, all ten tasks and all coding-agent models, verified September 10, 2026. Firecrawl: 76.9 MAXI, 90% task success, 8.3% validated friction; Bright Data: 71.8 MAXI, 86.7% task success, 3.3% validated friction; Zyte: 64.6 MAXI, 78.3% task success, 0% validated friction; Apify: 63.5 MAXI, 83.3% task success, 0% validated friction; Oxylabs: 62.3 MAXI, 78.3% task success, 6.7% validated friction. MAXI combines agent cost, completion time and validated friction. Task success is separate. Public-docs evaluation.

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Morphiq Labs retweeted
Aryan@whichmantechSep 11
How easily can coding agents use AI evaluation tools? 10 common tasks, public docs. MAXI: 1. @langfuse 20.7 2. @braintrust 20.1 3. @LangChain (LangSmith) 20 4. @rungalileo 18.3 5. @arizeai (Phoenix) 18.1 MAXI = agent cost, time & friction. Full results below.
Morphiq AI evaluation benchmark, all ten tasks and all coding-agent models, verified September 11, 2026. Langfuse: 20.7 MAXI, 66.7% code-quality pass rate, $0.99 agent cost, 177 seconds agent time, 1.7% friction; Braintrust: 20.1 MAXI, 70% code-quality pass rate, $1.04 agent cost, 195 seconds agent time, 8.3% friction; LangSmith: 20 MAXI, 76.7% code-quality pass rate, $1.07 agent cost, 158 seconds agent time, 11.7% friction; Galileo: 18.3 MAXI, 65% code-quality pass rate, $1.03 agent cost, 215 seconds agent time, 30% friction; Arize Phoenix: 18.1 MAXI, 56.7% code-quality pass rate, $1.15 agent cost, 210 seconds agent time, 33.3% friction. MAXI combines agent cost, time and validated friction. Documentation-based integration evaluation, not live API performance or evaluator accuracy.

ALT Morphiq AI evaluation benchmark, all ten tasks and all coding-agent models, verified September 11, 2026. Langfuse: 20.7 MAXI, 66.7% code-quality pass rate, $0.99 agent cost, 177 seconds agent time, 1.7% friction; Braintrust: 20.1 MAXI, 70% code-quality pass rate, $1.04 agent cost, 195 seconds agent time, 8.3% friction; LangSmith: 20 MAXI, 76.7% code-quality pass rate, $1.07 agent cost, 158 seconds agent time, 11.7% friction; Galileo: 18.3 MAXI, 65% code-quality pass rate, $1.03 agent cost, 215 seconds agent time, 30% friction; Arize Phoenix: 18.1 MAXI, 56.7% code-quality pass rate, $1.15 agent cost, 210 seconds agent time, 33.3% friction. MAXI combines agent cost, time and validated friction. Documentation-based integration evaluation, not live API performance or evaluator accuracy.

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Morphiq Labs retweeted
Aryan@whichmantechSep 10
How easily can coding agents use vector databases? 10 common tasks, public docs. MAXI ranking: 1. @upstash 85.1 2. @pinecone 84.5 3. @qdrant_engine 81.4 4. @weaviate_io 76.1 5. @turbopuffer 75.1 MAXI = agent cost, time & friction. Full results below.
Morphiq vector-search benchmark, all ten tasks and all coding-agent models, verified September 10, 2026. Upstash Vector: 85.1 MAXI, 86.7% task success, 0% validated friction; Pinecone: 84.5 MAXI, 86.7% task success, 1.7% validated friction; Qdrant Cloud: 81.4 MAXI, 80% task success, 1.7% validated friction; Weaviate Cloud: 76.1 MAXI, 71.7% task success, 0% validated friction; turbopuffer: 75.1 MAXI, 80% task success, 1.7% validated friction. MAXI combines agent cost, completion time and validated friction. Task success is separate. Public-docs evaluation.

ALT Morphiq vector-search benchmark, all ten tasks and all coding-agent models, verified September 10, 2026. Upstash Vector: 85.1 MAXI, 86.7% task success, 0% validated friction; Pinecone: 84.5 MAXI, 86.7% task success, 1.7% validated friction; Qdrant Cloud: 81.4 MAXI, 80% task success, 1.7% validated friction; Weaviate Cloud: 76.1 MAXI, 71.7% task success, 0% validated friction; turbopuffer: 75.1 MAXI, 80% task success, 1.7% validated friction. MAXI combines agent cost, completion time and validated friction. Task success is separate. Public-docs evaluation.

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Morphiq Labs retweeted
Ashish@itsashonarollMay 26
Agent visibility is the next distribution layer. The future traffic to APIs, SDKs, docs, CLIs, MCP servers, and developer tools will increasingly come from agents acting on behalf of humans. And because agent behavior is still heavily shaped by human workflows and developer ecosystems, human experience today gives a strong signal for what agent experience evolves into tomorrow. If agents can’t properly experience, interpret, and use your product through the internet, your distribution slowly collapses. Every tech company will eventually need to start optimizing for Agent Experience (AX). For consumer products, that may look like: CX <-> AX UX <-> AX For developer tools, it becomes: DX (Developer Experience) <-> AX (Agent Experience) What we’re building at @TryMorphiq helps shape that relationship. We help companies make their dev tools more discoverable, understandable, and usable for the next generation of coding agents.
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