Plan the build. Change the code. Design the part. Find AI tools for each job, and know what you still need to make and test.
38 tools · 6 kinds of work · Original sources
AI can help with the firmware. You still assemble, calibrate and test the hardware.
Explore the build →Start with the output your build needs.
Illustrations show learning concepts, not tested builds or tool outputs.
A chat assistant, an agent and a local model solve different parts of the job.
Use a hosted assistant such as ChatGPT or Claude to discuss a design, explain a datasheet or review a small code sample.
You needA clear question and the relevant source. Check the answer against that source before building.
Compare research tools →An agent can read files, edit code and run tools within its permissions. Installing the agent on your laptop does not mean its model runs there.
You needA saved working version, an exact target and a test command. Review changes before flashing or publishing.
Compare coding tools →A local runtime loads a compatible model onto your computer. You choose the model, memory budget and connections.
You needA suitable computer and time to test quality and speed on your actual task.
Check local AI requirements →DeepSeek, Qwen, Kimi and GLM come from Chinese developers and deserve consideration alongside the tools below. Country of origin does not tell you where a service processes your data, whether weights are available, or what fits your computer. Compare the exact model and provider.
Routes: hosted chat and API, plus published weights. The R1 release includes smaller distilled Qwen- and Llama-based models; these are different models from the full R1 and the current hosted service.
Explore: compare explanations of a known firmware fault. Thinking and tool support depend on the selected version and API mode. Check the model card and underlying licence for a distillation.
Current API options ↗ · R1 weights and distillations ↗Routes: hosted models through Alibaba Cloud Model Studio and downloadable releases. The family spans different text, coding and multimodal options; a family name alone does not specify image, audio or tool support.
Explore: try a smaller compatible model on a fixed data-extraction task, then compare a larger hosted option. Qwen3 open weights use Apache 2.0; verify the exact release licence and service terms rather than applying that to every Qwen product.
Hosted model catalogue ↗ · Qwen3 sizes and licence ↗ · Later Qwen releases ↗Routes: Kimi assistant, API, Kimi Code and published model weights are distinct ways in. Kimi Code is a coding product, not the name of every Kimi model.
Explore: request one tested change in a small repository. Check which model and tools the product actually uses. Downloadable large mixture-of-experts models can require substantial infrastructure; open weights do not imply laptop suitability.
Kimi assistant, API and research ↗ · Kimi Code setup ↗ · Example coding model card ↗Routes: hosted API options and published weights. Z.ai lists text, vision and other specialist models; these capabilities are not interchangeable across the GLM family.
Explore: compare code changes or, with a vision-capable version, extract a reading from your own display image. Inspect model size and deployment instructions: “Flash” or a low active-parameter count is not a RAM requirement.
Hosted capabilities ↗ · Published GLM models ↗ · Example multimodal model card ↗Representative exploration guides, not rankings or a complete model catalogue. Official sources checked 9 September 2026. Suggested experiments are CAIBI guidance; these models have not been comparatively benchmarked by CAIBI.
38 tools
Capabilities checked against official sources on 6 September 2026. The starting tasks and checks below are CAIBI guidance, not comparative test results. Follow each source for current plans and supported features.
No matching tool entries. Try firmware, CAD or research, or open the separate model-family guides.
Understand the source material, compare routes and identify what you still need to find out.
5 tools to compare
A conversational starting point for understanding a build, comparing approaches and working through calculations or code.
Ask it to extract prerequisites and unanswered questions from the original build guide before proposing changes.
Ask for sources and check them. A plausible part number, dimension or explanation is not physical verification.
Browser or app; features and usage limits depend on the account.
Keep your sources and results in build notes →An alternative for reasoning through documentation, analysing files and explaining a project or its code.
Provide the actual source files and ask which information is missing before creating a bill of materials.
Check quoted specifications against the supplied documents. Chat assistance and repository-editing agents are different workflows.
Browser or app; available features vary by plan.
Keep your sources and results in build notes →Multimodal assistance for exploring ideas, understanding supplied material and researching a topic through Google’s AI tools.
Compare two documented approaches to a desk display, keeping power, connectivity and enclosure requirements separate.
Images do not establish accurate dimensions. Check research citations and current component documentation.
Google account; capabilities and limits vary by plan and region.
Keep your sources and results in build notes →A source-oriented way to find documentation, alternatives and technical discussions before assessing a build.
Search for the exact board revision and its official documentation, then separate manufacturer sources from forum reports.
A citation can be relevant without supporting the claim beside it. Open the page and check the exact model and date.
Web and app; research features have account-dependent limits.
Keep your sources and results in build notes →A focused reference workspace for the manuals, guides and notes you collect around one build.
Create a notebook containing the build guide and component manuals; ask for a preparation checklist with source references.
Missing or outdated source material limits the answer. Check original diagrams and numerical specifications.
Google account; source and usage limits vary.
Keep your sources and results in build notes →Move from a conversation to reviewable changes in a working repository.
6 tools to compareA coding agent for working inside a repository, editing files and running available development checks.
Ask it to identify the board target and build command, then make one firmware change on a branch and compile it.
A successful compile is not a successful hardware test. Review changes before flashing and retain a known-working version.
CLI, editor and app workflows; account requirements vary.
Keep your sources and results in build notes →Repository-aware coding assistance through terminal and editor workflows, including file edits and commands.
Provide an ESP-IDF project and ask for a minimal fix to one reproducible compiler error, with the diff explained.
Control command permissions and inspect the diff. It cannot infer your actual wiring from the repository.
Installed developer tooling and an eligible account or API setup.
Keep your sources and results in build notes →An editor and coding agent that can search a codebase, modify files and run terminal commands.
Open a working firmware project, locate the display code and request one layout change with a compile check.
Review library choices, board configuration and generated commands. Keep hardware secrets out of prompts and committed files.
Desktop editor or CLI; usage depends on the selected plan and models.
Keep your sources and results in build notes →Coding help within supported editors and GitHub, from explanations to agent-driven repository changes.
Turn a documented firmware bug into a small issue with reproduction steps and acceptance checks.
Cloud tasks may not have your board or flashing tools. Distinguish automated software checks from tests on a connected device.
GitHub account and supported editor or GitHub workflow; quotas vary.
Keep your sources and results in build notes →Another terminal and editor agent for reading code, editing files and executing development commands.
Use a known-working project and ask it to document the setup, then implement one testable change.
Confirm model access and command permissions. Verify generated code against the actual SDK and device.
CLI or VS Code integration; account or compatible provider configuration required.
Keep your sources and results in build notes →
Source referenceConfigure a small pen plotter's axes and limits, verify direction and travel, then run a simple drawing.
Check the documented configuration for the exact controller and test motion without a cutting tool.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Choose between editable engineering geometry, CAD guidance and mesh generation.
11 tools to compare
Combines text/image-to-3D, vector generation and a CAD agent for dimensioned parts.
For a bracket, supply dimensions, hole centres and wall thickness; request an editable CAD output rather than an artistic mesh.
Check units, tolerances, wall thickness and fit. A printable-looking preview does not prove a mechanically useful part.
Browser service; credits and export access depend on the workflow and plan.
Keep your sources and results in build notes →Parametric CAD with a conversational agent, combining direct modelling and code-based geometry.
Create a simple mounting plate with named dimensions; change the hole spacing and inspect the resulting model.
Inspect constraints and geometry. Manufacturing feedback is not a strength calculation or approval for a load-bearing part.
Desktop application with browser exploration; AI access depends on plan.
Keep your sources and results in build notes →CAD and CAM with automated and AI-assisted design/manufacturing features inside a broader engineering workflow.
Start with a measured model and learn the relevant modelling or CAM operation before adding automation.
Feature access varies by licence or extension. Toolpaths still require machine setup, stock and collision checks.
Installed CAD/CAM software; check licence and extension eligibility.
Keep your sources and results in build notes →Documentation-based help for learning and using Onshape’s CAD features.
Ask how to constrain a sketch and make hole spacing editable, then perform and inspect those steps in the model.
Advisor guidance is not generated engineering geometry or a design decision. You remain responsible for the actual model.
Onshape workspace; check availability within your account.
Keep your sources and results in build notes →Create a sensor enclosure from measured board dimensions; inspect mounting holes and wall thickness, then print a fit prototype.
Model a simple bracket from measured dimensions and inspect the exported solid before printing.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Generate three sizes of the same electronics enclosure and compare STEP output with a real board and fasteners.
Generate two sizes of the same enclosure and inspect wall thickness and hole positions.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Prepare a shallow tray or fixture from a model, inspect roughing and finishing paths, then validate setup on the actual machine.
Preview toolpaths for a simple part and inspect stock, clearance and workholding before machining.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Make a parametric cable organiser with configurable cable diameter, spacing and mounting holes; print fit samples before a full batch.
Change one dimension in a small practical model and inspect clearances before printing.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Nest a set of laser-cut enclosure panels, accounting for material, spacing and intended grain direction before cutting.
Nest a few known SVG parts and check units, spacing and kerf allowances before cutting.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Model a four-bar linkage for a kinetic sculpture, inspect its path and interference, then build a low-load prototype.
Constrain a simple linkage and inspect its range of motion before printing parts.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Create a repeatable mounting-pattern operation or export a reviewed set of parts after a parameter change.
Automate one change to a known model and compare the output with the original dimensions.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Connect components, electrical requirements and firmware before committing to a board.
6 tools to compare
Connect the hardware
Learning concept. Check the design, interfaces and physical output.Browser PCB design with AI assistance for component research, schematics, bills of materials and board layout.
Begin with a simple sensor board and ask for a proposed power architecture and component rationale before routing.
Verify pinouts, footprints, voltage limits, layout and rule checks. Do not order boards solely from an AI-generated schematic.
Browser eCAD service; plan and manufacturing requirements vary.
Keep your sources and results in build notes →Circuit design, wiring and code assistance with simulation for supported embedded hardware.
Model one sensor and its serial output before attempting the complete device.
Simulation covers supported models, not every physical failure. Check voltage levels and the real board revision.
Browser service; simulation support and usage depend on components and plan.
Keep your sources and results in build notes →An AI-assisted hardware workspace for Arduino, ESP32 and Pico projects, bringing parts, wiring and code together.
Choose one supported board and one output device, then build a minimal prototype before adding features.
Confirm hardware support, pin assignments and library versions against documentation before assembly.
Hardware IDE; verify current platform and board support.
Keep your sources and results in build notes →A modular electronics platform whose vendor describes generating board layouts and ESP32 firmware from a project description.
Read the module reference and check that the required inputs and outputs are supported before ordering hardware.
Vendor documentation reviewed; generated firmware and physical operation have not been independently tested by CAIBI. Compatibility with arbitrary third-party hardware is not established.
Hardware offered for preorder. Confirm delivery, kit contents and ongoing platform access before purchasing.
Keep your sources and results in build notes →Prepare a board's documented output set after schematic and layout review, with electrical and design-rule reports retained.
Run rule checks and manufacturing exports on a known KiCad example before adapting a board.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Prototype a rotary-dial menu and status lights, verify input handling, then build and test the same supported circuit.
Simulate one button and output, then deliberately test a fault before wiring a real board.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Create a visual design, then prepare it for the actual printing or cutting process.
7 tools to compare
Prepare a physical output
Learning concept. Check the design, interfaces and physical output.Image and editable vector generation for artwork that can move into printing, engraving or craft workflows.
Generate a simple motif, inspect the exported vector in a vector editor and prepare it in the machine’s software.
Artwork is not a cutting toolpath. Check closed paths, duplicate lines, minimum detail, rights and material suitability.
Browser/Adobe workflows; generative credits and features vary by plan.
Keep your sources and results in build notes →Text- and image-to-3D for mesh-based objects, concepts and decorative forms.
Try a decorative object first, then inspect scale, mesh integrity and supports in your modelling/slicing tools.
Generated meshes are not dimensioned parametric CAD. Do not assume accurate fits, internal features or structural strength.
Browser/API service; generation and export allowances vary.
Keep your sources and results in build notes →
Source referenceTurn a hand-drawn profile into a mesh, validate it and 3D-print the resulting vase or desk object.
Create one simple shape, inspect mesh dimensions and thickness, then test a small print.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Blender software is free; printing/materials and creator download terms require separate checking.
Keep your sources and results in build notes →
Source referenceGenerate and edit owned artwork for a physical sign, printed panel or engraved object; proposed downstream use only.
Create one original motif and inspect it at the intended print or transfer size.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Local computer/GPU or optional hosted service; plugin is GPL-3.0, model terms vary.
Keep your sources and results in build notes →
Source referenceGenerate an object mesh, repair geometry, validate dimensions and test-print; source proves 3D asset generation but not printable quality.
Generate one simple object and check watertightness, wall thickness and scale in a modelling tool.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Model execution requires suitable compute; local versus rented GPU cost must be measured.
Keep your sources and results in build notes →
Source referenceScan a real object and assess whether an exported mesh can become a remixed or reproduced physical print; source capability is capture, not dimensionally proven fabrication.
Scan one matte object and compare known dimensions before using it as a fabrication reference.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Compatible phone; subscription/export limits depend on capture mode and current plan, not priced here.
Keep your sources and results in build notes →Create a small original patch, preview its stitch plan, then stitch on scrap fabric and adjust underlay, density and sequencing.
Digitise one small shape, preview the stitch plan and sew a test on scrap fabric.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Choose where the model runs, check the memory it needs, then connect it to your application. Ollama is a runtime; Llama is a family of models.
Understand memory, context and local setup →3 tools to compare
Connect a model to a device
Learning concept. Check the design, interfaces and physical output.A way to run compatible open models locally and integrate them into your own software.
Use it to explain a small source file offline before connecting it to a build tool or private-data workflow.
Memory, speed and capability depend on the model and hardware. Cloud models or connected tools can still send data off-device.
Local installation and model download; hardware requirements vary by model.
Keep your sources and results in build notes →Build an offline voice or caption interface into a custom device; toolkit supports embedded and mobile deployment, hardware integration remains proposed.
Try one supported model on the target device and measure latency before designing hardware.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Target hardware and development time; software/model licences and memory needs vary.
Keep your sources and results in build notes →Collect accelerometer gestures for a handheld controller, train against real examples, and test unseen users and everyday non-gesture motion.
Record a small labelled gesture dataset and test against separate recordings before deployment.
Design or software resource. A fabrication-ready physical result has not been verified by CAIBI.
Software access and any optional AI service must be checked at review time. Hardware, materials, machine access and failed prototypes are separate; no complete build cost verified.
Keep your sources and results in build notes →Start with a task you can check. A local model can be useful without matching a hosted assistant across every task.
The provider runs the model. Your computer does not need memory for its weights. You rely on the service’s access, usage limits and data terms.
Useful when you want to get started without managing model downloads or inference hardware.
You supply the compute, storage and memory. Smaller models can fit more easily, but quality, speed and tool support need checking against your task.
Useful for a bounded offline workflow or an application where you want control over model hosting.
A local app can call a cloud model. A local model can use web search or external tools. Check where requests go before treating the workflow as offline or private.
How Ollama cloud models work ↗Model: the downloaded weights that generate answers. Llama is one model family. LM Studio also supports families such as Qwen and Mistral. Check the exact version, size, licence and required features, including tool calling, before downloading.
Runtime: software that loads and runs compatible models. Ollama is one option. LM Studio offers a desktop interface; llama.cpp is another inference engine. These are alternative setup routes, not a performance ranking.
Agent: an application that uses a model and tools to act on a task. Check file, command and network permissions separately from model quality. See a concrete agent permission system ↗
RAM is system working memory; VRAM is dedicated graphics memory. Apple silicon uses a shared unified memory pool, which the operating system and other apps also need.
Allow room for model weights, the runtime and the conversation cache (KV cache). The model’s download size is not the complete memory requirement. More context and simultaneous requests can require more memory.
Quantisation stores weights at lower precision to reduce memory use. It can affect output quality; a smaller download is not evidence that it will handle your task well. Quantisation explained ↗
For an initial reference, LM Studio recommends 16 GB or more RAM, and on Windows at least 4 GB dedicated VRAM. These are application recommendations, not a guarantee for any particular model or agent. Check your exact operating system and hardware in the system requirements ↗.
Tokens are chunks of text used to measure input and output. The context window is the model’s working space for a request: instructions, conversation, supplied documents and tool results all take space. Output limits are separate; a large context allowance does not mean an equally long answer.
A model’s advertised maximum may exceed the runtime’s configured context or your available memory. Longer contexts increase memory demand. An agent working through many files may need much more context than a short chat.
In Ollama, inspect the active model with ollama ps to check the configured context and CPU/GPU split. If performance falls, try a smaller model or shorter context and repeat the same task. Ollama context settings ↗ · How context windows work ↗
For a device build, distinguish the controller from the model host: a small controller can send a request to a computer or service without running that model itself.
Start with a checkable data exercise →Local inference can keep prompts on your machine, but downloaded models, cloud options, connected tools and logging each need checking. Ollama provides a local-only setting that disables its cloud features. Ollama privacy and local-only settings ↗
Local use still consumes storage, electricity and setup time. Hosted subscriptions and API billing are different access routes; check the current plan before assuming a subscription covers your own application.
Technical guidance checked against the linked official documentation on 9 September 2026. The trial above is CAIBI guidance, not a hardware benchmark.
Could your project read a display, answer questions about its manual, or respond to a spoken request? You can explore the idea before you know how to build it. Ask an assistant to break it into parts, then try one part you can check.
Supply context when a few relevant files fit. Retrieval-augmented generation (RAG) finds relevant passages from a larger collection and supplies them to the model. Fine-tuning trains on examples to change behaviour; it is a separate workflow from looking up current documents.
For a workshop assistant, first try ten known questions against a few source manuals. Require page references and include questions the manuals cannot answer. If relevant passages are missing, improve retrieval before changing the model. Consider fine-tuning only with a repeatable task, suitable training examples and a separate evaluation set.
Build and inspect a retrieval workflow ↗ · What fine-tuning involves ↗Image understanding, image generation, speech recognition and speech output are different capabilities. Check both the exact model and the interface: an app may combine several models. A longer reasoning mode is an option to test, not a guarantee of correctness.
Try reading a display photograph, transcribing a workshop note or explaining a known fault. Test glare, noise, units and ambiguous inputs. A generated image is not dimensioned CAD, and identifying a component from a photo does not verify its pinout. Return to the source and a physical check.
Example capability catalogue ↗A tool call proposes a structured action; your application runs it. An agent adds a loop of decisions, tool results and further actions. Model Context Protocol (MCP) is one way for an app to connect an assistant to tools and information. A connection does not make those tools trustworthy or improve the underlying model by itself.
Start with read-only access to one project. Add a bounded edit, a test command and a rollback. Keep credentials out of prompts; treat instructions found inside retrieved files or web pages as untrusted. Require approval for publishing, spending or operating hardware.
Try a smaller model for extraction and a stronger model for difficult cases. Several agents can divide independent work, but also add cost and conflicting changes. Compare their combined result with one agent on the same task before expanding permissions.
How MCP connects an app to tools ↗ · How tool calling works ↗ · A concrete permission system ↗Compare the cost per accepted result, including failed attempts, review time and retries. Record model/version, input and output size, reasoning settings, response time and correctness. Repeat representative cases; one impressive answer or a vendor leaderboard does not establish reliability for your build.
For APIs, check input/output rates, caching, tool charges, limits and availability. For local use, include hardware, electricity, storage and maintenance. A mixture-of-experts model activates only some parameters per token, but that active count alone does not tell you how much memory its weights need. Test the intended context and concurrent workload.
Keep an exportable test set and saved outputs. Re-run it before switching model versions or providers, and choose a failure state when the service is unavailable.
Record your comparison →Open weights mean model parameters are available; they do not guarantee access to training data, unrestricted licensing or free hosted use. Read the exact model licence, upstream conditions and service terms before distributing a modified model or building a product around it.
Check the actual operator, processing region, retention, training use, logging and connected tools for your chosen account and endpoint. A third-party host of the same weights can have different terms from the developer. Local inference can still send data through cloud tools.
Test the languages, units and topics your project uses. Record uncertainty, omissions and refusal behaviour. Keep data export and a replacement-provider route in mind; API compatibility does not guarantee identical tool calls or outputs.
Inspect the model and provider sources →Take saved API responses, including missing and invalid values. Ask two models to produce the same three fields.
Keep: a test set, expected answers and a result table. Check validity, units, time and cost before choosing.
Prepare the failure cases →Use a few public manuals and ask questions with known page references. Add an unanswerable question.
Keep: answers linked to passages and a list of misses. Progress from supplied excerpts to retrieval when the collection grows.
Follow a retrieval implementation ↗Prototype a typed command, then consider voice or an image. Convert it into a small allowed set of actions.
Keep: validated commands, an unknown-input state and a manual override. Test in simulation before connecting physical outputs.
Build predictable control states →Exploration briefs, not completed CAIBI lessons or tested device designs. The linked sources support the technical concepts; choose a bounded experiment and record the result.
Three routes from an AI-assisted change to something you can inspect, test and improve.
Use a general assistant to clarify the brief, then a CAD workflow such as Zoo, Makerful or scripted OpenSCAD. Keep width, thickness and hole spacing editable.
Use Codex, Claude Code, Cursor, Copilot or Kimi Code in an existing project. Give the agent the board revision and a known-working build command.
Use an assistant to understand an API response and draft parsing code. Keep sample data separate from the live source and decide what the device shows when a request fails.
Tell the tool what you have, what must change and how you will check it. Keep source claims separate from its suggestions.
Choose an assessed build →I am working from: [source URL and version] My board, materials and tools: [exact details] The one outcome I need: [measurable result] Constraints: [dimensions, power, budget, interfaces] First identify missing information. Do not invent it. Propose a small change and explain your assumptions. Show how to inspect or test the output. State what still needs to be checked on the physical build.
Save what you tried, what worked and what failed. That is the useful evidence another maker can learn from.
Start private build notes →Share a documented result →Explore these sources, then use the ideas board to propose what is missing, develop a completion route or share a documented result. A source listing is not a CAIBI assessment of everything it publishes.
Follow conversational CAD developments, then check the exported geometry and physical fit.
Zoo on X ↗Explore PCB design workflows and documentation. Manufacturer claims still need circuit and hardware checks.
Flux video tutorials ↗Explore training and deploying models on devices. AI inference is a different role from AI helping design the build.
Edge Impulse on X ↗Follow open robot-learning software and documented hardware routes. An ambitious path with assembly and calibration work.
Explore using AI for Arduino and ESP32 coding. Confirm pin assignments and library versions, compile in small steps and test on the actual hardware.
Source links checked 16 September 2026.
Explore ideas to develop → · Found a build, useful source or something we missed? Send it to CAIBI →