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Use Meshy 8K Texture Resolution Without the Old hd_texture Flag

#meshy #3d+3

Meshy added a textureresolution parameter for 2K, 4K, and 8K output across its 3D generation and retexture APIs, while keeping hdtexture only as a compatibility alias. Meshy's July 2026 API changelog adds a practical migration point for teams generating higher-resolution 3D assets: use the new textureresolution parameter instead of relying on the older hdtexture boolean. The new parameter supports 2k, 4k, and 8k on Image to 3D, Multi-Image to 3D, Text to 3D Refine, and Retexture endpoints. Meshy says 8K base-color generation costs 15 credits, while 2K and 4K remain 10 credits. At 8K, PBR maps are generated at 4K and no emission map is produced, so pipelines that expect a full PBR bundle should check output fields before swapping defaults. Use this as a quick API cleanup item if your scripts still send hdtexture: true. Meshy still honors it as equivalent to textureresolution: "4k", but the new parameter is clearer, supports 8K explicitly, and makes pricing behavior easier to review before batch generation.

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Tune ElevenLabs Music Models from Your Own Audio

Text: AI-generated

#elevenlabs #music-generation+5

AI-generated · Automatically published by LinkLoot. ElevenLabs added Music Finetunes API endpoints for creating, listing, checking, updating, and deleting custom music finetunes. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. ElevenLabs now exposes Music Finetunes through its API, which makes this useful for teams building repeatable music-generation workflows rather than one-off prompts. The July 20 changelog says the new API can create a finetune from uploaded audio, list accessible finetunes, retrieve training status, update metadata and visibility, and delete a finetune. Music generation SDK methods now expose finetuneid, so an app can route future compositions through a specific trained style or catalog. Use it when you need controlled brand music, recurring creator formats, or internal audio experiments where the same reference material should drive multiple generations. Check rights on the uploaded audio first, keep training sets documented, and treat visibility settings as part of the release checklist. How to evaluate it: Read the July 20 changelog to confirm the endpoints and SDK support. Check the Create Music Finetune reference for required fields such as name and primarygenre. Start with a small, rights-cleared audio set and verify training status before using finetuneid in production generation calls.

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Migrate Supabase logs.all queries before the September cutoff

#supabase #api+3

Supabase is removing the old logs.all Management API endpoint on September 23, 2026; this resource points developers to the new ClickHouse-backed logs endpoint and migration notes. Supabase published a migration note for teams that query the Management API logs.all analytics endpoint directly. The old endpoint is scheduled for removal on September 23, 2026. Use this as a bookmark if you have scripts, internal dashboards, observability jobs, MCP tooling, or agent workflows that call .../analytics/endpoints/logs.all. What changes: Endpoint path changes from analytics/endpoints/logs.all to analytics/endpoints/logs. Queries must use ClickHouse SQL. Logs now come through a unified logs table. Source-specific queries need a sourcename filter, such as WHERE sourcename = 'edgelogs'. Nested fields move from metadata arrays to a flatter logattributes map. This is not a dashboard change. Supabase says the dashboard Logs Explorer is not affected. The risk is custom code that calls the Management API endpoint directly and assumes the old table-per-source structure. Check your codebase for logs.all, analytics/endpoints/logs.all, and old metadata unnesting patterns, then convert the queries against Supabase's new logs endpoint before the cutoff.

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Use OpenRouter Free Models Router for zero-cost model trials

#openrouter #free models+3

Route quick prototypes to currently available free OpenRouter models with one OpenAI-compatible model slug. OpenRouter's openrouter/free router is a practical shortcut for builders who want to test AI features before committing to a paid model. Instead of choosing one free model manually, you call the openrouter/free model slug and OpenRouter selects from the free models that match the request requirements, including capabilities such as image understanding, tool calling, or structured outputs when available. Use it for early prototypes, internal demos, extraction tests, routing experiments, and cost-sensitive agent tasks where occasional model variation is acceptable. The official Free Models collection is also useful as a current shortlist when you need to pin a specific free model instead of using the router. Evaluation checklist: Start with low-risk test data, not customer secrets. Log the selected model and output quality during trials. Move production workflows to a pinned model once behavior needs to be stable. Check the model page before assuming context length, tool support, license, or training-data terms. Caveats: free capacity can change, rate limits may apply, and some provider terms allow inputs or outputs from free usage to be used for model improvement. Treat this as a fast discovery and prototyping lane, not a guaranteed production SLA.

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Test OpenRouter Auto Beta for Task-Aware Model Routing

#openrouter #model routing+3

OpenRouter's Auto Beta router classifies each request and routes it to a model based on task type, aggregate usage, and a configurable cost-quality tradeoff. What it is OpenRouter Auto Beta is a task-aware router for teams that do not want to hard-code one model for every prompt. You call openrouter/auto-beta, OpenRouter classifies the request, then routes it to a model selected from live task-type rankings and your cost-quality setting. Why bookmark it It uses the same OpenAI-compatible API path as other OpenRouter models, so testing does not require a new SDK. The model page exposes a 2,000,000-token context window and the docs explain session stickiness, model customization, benchmarks, and response metadata. Pricing is charged at the rate of the routed model, not as a separate flat router price. The response can show which model handled the request, which matters for audit logs and regression checks. Good first tests Try it on varied workloads: short support replies, code review, long-context research, extraction, and tool-heavy agent calls. Log the chosen model, total cost, latency, failure modes, and answer quality against your current fixed-model route. Caveats Treat this as routing infrastructure, not a quality guarantee. Pin models for regulated, reproducible, or benchmarked workflows. Use Auto Beta where adaptive routing is more valuable than exact provider control.

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Make Cloudflare Workflows retry around rate limits instead of fixed timers

#cloudflare #workflows+4

Cloudflare Workflows now lets each step calculate its next retry delay from the failed attempt and error, useful for rate limits, Retry-After handling, and flaky downstream APIs. Cloudflare added dynamic retry delays to Workflows on July 9, 2026. Instead of picking only a fixed delay plus constant, linear, or exponential backoff, a Workflow step can now pass a function to retries.delay and calculate the next wait from the current attempt and the thrown error. Use this when a workflow calls APIs with different failure modes: wait longer on rate-limit errors, retry faster after a short network failure, or map provider guidance such as Retry-After into your next delay. This is a practical fit for customer sync jobs, AI-provider orchestration, payment/CRM integrations, and any durable workflow that should avoid both blind hammering and unnecessary waiting. Check before using Confirm your Workflow code is using the current Cloudflare Workflows API and docs. Treat error-message matching as a fallback; prefer structured error data when your SDK exposes it. Add a maximum retry count and clear terminal-error path so bad credentials or malformed payloads do not loop. Test against one intentionally rate-limited API call before relying on it for paid providers. Sources Cloudflare changelog: dynamic retry delay support for Workflows. Cloudflare Workflows docs: sleeping, retrying, and the retries.delay function shape.

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Get 80+ Free NVIDIA-Hosted AI Models with Your Own API Key

#NVIDIA #AI Models+4

This resource highlights how to access a broad set of NVIDIA-hosted AI models with your own API key. It is useful for builders comparing free model access, hosted inference options, and practical experimentation routes. A compact workflow for trying Nvidia-hosted AI models for free while the offer is available. This is useful if you want to test models like GLM, Kimi, or DeepSeek from your IDE or your OpenClaw setup without building the integration from scratch. Quick setup Best use cases quick model comparison testing API-based coding workflows prototyping with hosted inference wiring models into IDEs like Cursor or similar tools experimenting inside an OpenClaw instance Compact takeaway If you want a low-friction way to try a broad range of current AI models, Nvidia Build is a strong shortcut: create an account, generate a key, copy the example code, and plug it into your workflow.

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More from this topic

Use Meshy 8K Texture Resolution Without the Old hd_texture Flag

Tune ElevenLabs Music Models from Your Own Audio

Migrate Supabase logs.all queries before the September cutoff

Use OpenRouter Free Models Router for zero-cost model trials

Test OpenRouter Auto Beta for Task-Aware Model Routing

Make Cloudflare Workflows retry around rate limits instead of fixed timers

Get 80+ Free NVIDIA-Hosted AI Models with Your Own API Key

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