How a 200-Controller Laravel SaaS Was Deployed on Shared Hosting Without SSH

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A development team successfully ran a large-scale Laravel application — featuring over 200 controllers, 130+ models, CRM, payments, and campaign tooling — on standard cPanel shared hosting with no SSH access, no Artisan CLI, and no queue workers. Deployments were handled via zip uploads through the file manager, with cache clearing managed through a secret-token-protected HTTP route as a substitute for Artisan commands. Database migrations were rewritten as idempotent SQL files safe to execute multiple times, since running standard migrate commands was not possible on the constrained environment. The team also encountered unexpected bugs, including a Blade parser issue where CSS media queries inside directive parentheses caused raw output above the doctype, and a production outage triggered by an unquoted space in an .env variable. Despite the limitations, the constraints forced modular, self-contained table design, which later allowed two modules to be reused in other projects without modification.

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MeshDrive 2.0 gives AI agents isolated local file storage via MCP on Linux

MeshDrive 2.0 is a local-first Linux storage tool built on JuiceFS and SQLite that provides AI agents with controlled access to a dedicated file system. Released by developer Hardik94 on GitHub, version 2.0 adds a Model Context Protocol (MCP) server enabling AI clients such as Cursor, Claude Code, and Hermes to read, write, and list files through a defined set of tools. Agents are restricted to an isolated root directory by default, preventing them from accessing files outside the designated storage area. The free tier requires no cloud connectivity, keeping all data on local disks, and includes a web-based file browser accessible at localhost. Installation is supported on Ubuntu and Debian amd64 systems running Python 3.10 or later with FUSE support.

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ProgrammingDEV Community ·

Best Practices for Defining AI Agent Skills: Structure, Naming, and Invocation

AI agent skills are used to automate repetitive, precisely describable processes, and must be carefully structured to minimize the risk of a model generating incorrect outputs. Skills can be invoked either explicitly by a developer or implicitly by the agent itself, and each one loaded into a session consumes context, making selective use important. Each skill must reside in its own dedicated directory, with the directory name matching the skill's name, and must contain a SKILL.md file holding its definition. The skill's YAML header requires at minimum a name — formatted in lowercase kebab-case between 1 and 64 characters — and a description of up to 1,024 characters specific enough for the agent to match it to user intent. Optional fields such as license, version, and context can further refine how a skill is managed, deployed, and legally used across different environments.

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ProgrammingDEV Community ·

How Redis Sliding Windows and Token Quotas Protect LLM API Spending

Rate limiting LLM APIs differs fundamentally from conventional APIs because a single model call carries real monetary cost, and agent loops can fire hundreds of requests autonomously. Developers must track both request counts and token usage separately, enforcing whichever limit binds first, since a one-line prompt and a 200-page document summarisation both count as one request but differ vastly in cost. Tenant identifiers used in rate-limit keys must be sourced from authenticated sessions, never from the request body, to prevent callers from manipulating or exhausting other tenants' quotas. Fixed time windows expose a known boundary exploit where a full quota can be consumed twice in rapid succession, making Redis sorted-set sliding windows a more reliable alternative when executed atomically via Lua scripts. Because actual token costs are only known after a model responds, the recommended approach is to reserve an estimated token count before the call and reconcile the difference once the response returns.

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ProgrammingDEV Community ·

tanstack-fetch: A Typed Fetch Client Designed to Simplify TanStack Query HTTP Layer

A developer has released tanstack-fetch, an unofficial, typed fetch client built specifically to complement TanStack Query in JavaScript and TypeScript applications. The library addresses common pain points that arise as apps scale, including authentication, typed error handling, request cancellation, retries, and server-sent events. Unlike native fetch, which does not throw on HTTP error status codes like 404 or 500, tanstack-fetch automatically converts HTTP failures into structured FetchError objects that integrate cleanly with TanStack Query's error state. The package can be configured with a base URL, token retrieval logic, and status-specific handlers, reducing repetitive boilerplate across query functions. It requires Node 18 or later in server environments and is available via npm alongside @tanstack/react-query.

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