Seamless.AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Seamless.AI AI

Will you switch to an AI-native Phone?

submitted by /u/No_Sheepherder_6908 [link] [comments]

every blog at my job is AI written and i can literally smell the em dashes from here

ngl i am SO done with AI slop at my firm and i need your hacks rn 😭 ok so i’ve been sitting here trying to read through our company’s blog posts and i genuinely cannot do it anymore. no cap, every single piece of content going out right now is dripping in AI. you know exactly what i’m talking about. the adjectives won’t stop. groundbreaking. transformative. seamlessly. robust. innovative solutions. i swear if i read “in today’s fast-paced landscape” one more time i’m actually going to leave my body. and the em dashes!! every. single. blog. has them scattered everywhere like confetti. it’s the number one AI giveaway and somehow nobody at my firm has noticed?? our firm used to have a voice. now every blog reads like ChatGPT and Claude had a baby and that baby went to a corporate buzzword bootcamp. it’s giving nothing. we are so cooked. so here’s what i actually need from you guys. what are your real tricks for making AI output sound like an actual human wrote it? i use Claude and ChatGPT daily and i want the prompts, the workflows, the secret sauce. stuff like: •how do you kill the em dash obsession •how do you stop the adjective stacking •any prompts that make it write more like a real person with opinions and a bad day drop your tricks in the comments. the AI writing era isn’t going anywhere so we might as well make it not suck fr fr 🙏 submitted by /u/KingOfTheGrandLine [link] [comments]

Claudy: A Rust-based Power-Tool for Claude Code (Profile Switching, MCP Bridge for Local Agents & Token Analytics)

Hi everyone, I love the Claude Code CLI, but I found myself constantly fighting with environment variables and wanting to use my own local agents or different engines (Gemini, Codex, etc.) within its ecosystem. Inspired by clother, I built Claudy in Rust to turn Claude Code into a truly flexible, multi-model power tool. Here are the 3 core features that make Claudy unique: 1. Instant Provider & Mode Switching Stop manually messing with CLAUDE_CONFIG_DIR or env vars. Claudy manages profiles and modes natively. You can switch between different provider setups and environments instantly, and Claudy handles the seamless execution of the Claude Code process with the correct context. 2. MCP Bridge for Local AI Agents (JSON-RPC) This is the heart of Claudy. It acts as an MCP (Model Context Protocol) bridge, allowing you to use local AI coding agents—like Gemini, Codex, or even Cursor's agent—directly inside Claude Code via JSON-RPC. Now you can leverage Claude Code's UX while using specialized local engines as your backend agents. 3. Visual Token Analytics (GUI) Monitoring costs and usage shouldn't be a guessing game in the terminal. Claudy includes an Analytics GUI that lets you inspect token usage and traffic visually. It gives you a clear breakdown of how your models are consuming resources, making it much easier to optimize your workflow. Why Rust? Since it handles real-time JSON-RPC bridging and manages sub-processes, I chose Rust to ensure there's absolutely zero overhead or latency in the communication loop. If you want to take your Claude Code setup to the next level—especially if you're experimenting with MCP and local agents—I'd love for you to give it a spin. GitHub: https://github.com/epicsagas/claudy Feedback and contributions are always welcome! 🚀 submitted by /u/adobv [link] [comments]

Windows users, what setup are you currently using to run multi-agent systems?

I’m currently using Orca as my terminal tool to run multiple worktrees. I’m still testing it out. It seems promising, but I feel like I haven’t quite reached the state of the art yet when it comes to programming with AI across multiple worktrees in a seamless way. I used to use WSL so I could use tmux, but now I’m increasingly testing the native Windows environment. I’d like to hear from other users about how they’ve been programming with AI on Windows. Whether they use the Windows terminal itself, WSL, tmux, or another Windows-compatible tool like Warp, Orca, etc. submitted by /u/madpeppers013 [link] [comments]

Claude will not finish this specific Deep Research task

For multiple days now, using multiple models and settings on claude.ai, I have been unable to get a successful deep research session back on the below prompt. It does the thinking, scans anywhere from ~750-2,000 sources, thinking/notes/progress all looks good. ...then it hangs...for hours. And then dies. Mostly with the red "Something went wrong" text. One time I saw the "Boom. research complete" note, but no document or summary was output. I've never had this with any other deep research task. Just seems to be this specific ask or something preventing it. Any ideas whats going on? --- # Deep Research Prompt: Complete Claude Code Capability & Configuration Atlas ## Role You are a meticulous technical researcher building the definitive, exhaustive, and **currently-valid** reference for everything that can be configured, customized, toggled, extended, or controlled in **Claude Code** (Anthropic's terminal-based agentic coding tool, package `@anthropic-ai/claude-code`). This is not a tutorial. This is a **complete capability atlas** — every knob, dial, file, flag, env var, hook, magic word, permission, integration, and undocumented-but-real feature. ## Objective Produce a single, comprehensive knowledge base covering **100% of Claude Code's configurable surface area**, with every entry **validated as present in the latest stable release** and **sourced** to an authoritative location. Anything deprecated, removed, renamed, or unverifiable must be **excluded** from the main catalog (and instead listed in a separate "Removed / Deprecated / Unverified" appendix with the evidence trail). ## Authoritative Sources (in priority order) 1. Official docs: `https://docs.claude.com/en/docs/claude-code/*` and `https://docs.anthropic.com/en/docs/claude-code/*` 2. Official GitHub repository: `https://github.com/anthropics/claude-code` — especially: - `CHANGELOG.md` (most recent entries define "latest") - `README.md` - Release tags / releases page - Open & recently-closed issues for behavioral edge cases 3. Anthropic engineering blog posts and announcements on `anthropic.com/news` and `anthropic.com/engineering` 4. The npm package metadata and any bundled `--help` output 5. Anthropic's Claude Code SDK docs (TypeScript and Python) 6. Anthropic Cookbook / reference repos under the `anthropics` GitHub org **Lower-trust sources** (community blogs, third-party tutorials, Reddit, X posts) may be used **only** to surface candidate features for investigation — every such candidate must then be re-verified against an authoritative source above before it earns a place in the main catalog. If a community claim cannot be authoritatively confirmed, file it under "Unverified." ## Scope — Categories To Exhaustively Cover For each category, enumerate **every** option, not just the popular ones. ### 1. Installation, Distribution & Runtime - Install methods (npm global, native installer, Homebrew, etc.) per OS - Supported OSes, terminals, shells, Node.js versions - Update mechanism, channel selection, version pinning - Uninstall and clean-state procedures - Working directory / trust prompts on first run ### 2. CLI Invocation - Every flag and option of the `claude` binary (e.g., `-p`/`--print`, `-c`/`--continue`, `-r`/`--resume`, `--model`, `--allowedTools`, `--disallowedTools`, `--permission-mode`, `--dangerously-skip-permissions`, `--output-format`, `--input-format`, `--verbose`, `--mcp-config`, `--add-dir`, `--session-id`, `--append-system-prompt`, etc.) - Subcommands (`claude config`, `claude mcp`, `claude doctor`, `claude update`, `claude migrate-installer`, etc.) — full subcommand tree - Stdin/stdout behavior, exit codes - Headless / non-interactive mode semantics - Streaming JSON input/output formats and schemas ### 3. Settings Files (Hierarchy & Schema) - Every settings file location and its precedence: enterprise managed → user (`~/.claude/settings.json`) → project shared (`.claude/settings.json`) → project local (`.claude/settings.local.json`) - Full JSON schema: every key, type, default, allowed values, scope - Examples include but are not limited to: `model`, `apiKeyHelper`, `permissions` (allow/deny/ask, additionalDirectories, defaultMode), `env`, `hooks`, `statusLine`, `outputStyle`, `cleanupPeriodDays`, `includeCoAuthoredBy`, `forceLoginMethod`, `disableAllHooks`, `enableAllProjectMcpServers`, `enabledMcpjsonServers`, `disabledMcpjsonServers`, etc. - How merging works across the hierarchy (override vs. union) ### 4. Environment Variables - Every recognized env var: `ANTHROPIC_API_KEY`, `ANTHROPIC_AUTH_TOKEN`, `ANTHROPIC_MODEL`, `ANTHROPIC_SMALL_FAST_MODEL`, `ANTHROPIC_BASE_URL`, `ANTHROPIC_CUSTOM_HEADERS`, `CLAUDE_CODE_USE_BEDROCK`, `CLAUDE_CODE_USE_VERTEX`, `CLAUDE_CODE_SKIP_BEDROCK_AUTH`, `CLAUDE_CODE_SKIP_VERTEX_AUTH`, `DISABLE_TELEMETRY`, `DISABLE_ERROR_REPORTING`, `DISABLE_NON_ESSENTIAL_MODEL_CALLS`, `DISABLE_AUTOUPDATER`, `DISABLE_BUG_COMMAND`, `DISABLE_COST_WARNINGS`, `BASH_DEFAULT_TIMEOUT_MS`, `BASH_MAX_TIMEOUT_MS`,

I built an open-source GPT Image & Video Generator web client using OPFS (Zero DB)

Hey guys, I'm a French frontend developer and a massive open-source geek. I love building core tools for specific tech niches, but recently I just wanted a cleaner, faster way to generate AI media without dealing with heavy UIs or expensive subscription lock-ins. So, I built GPT-Images. It's a fully open-source web interface for image and video generation. You just plug in your own OpenAI API key, and the app handles the rest. The architecture is what I'm most hyped about: Tech Stack: Svelte 5, SvelteKit, and Tailwind 4. The whole thing hosted on Cloudflare Workers, I use bun pm as it's faster to install and pretty stable for my case. Zero Database: I'm using the Origin Private File System (OPFS) to handle all media storage directly in the browser. It's fully local, and respects your privacy 100%. Features: Secure API key management, seamless media generation (both images and video), and a clean grid/lightbox UI to manage your outputs. I tried to keep the codebase as clean and strictly typed as possible. If just want a chill local UI for your AI generations, feel free to check it out and fork it. Repo: https://github.com/Ayfri/GPT-Images Site: gpt-images.ayfri.com submitted by /u/Ayfri [link] [comments]

I got tired of Claude generating "we are passionate about innovation" on every landing page. Here's what fixed it.

Every "AI website builder" I tried produced the same template fill: "We are passionate about innovation" "Cutting-edge solutions for the modern era" Three feature cards titled Innovative, Reliable, Dedicated with one sentence of fluff each Fabricated testimonials with names like "Sarah J." and "Mike T." Lorem ipsum still sitting in the about page when the demo screenshot was taken For a service agency, that output is worse than useless. It ships, it doesn't convert, and you spend the weekend rewriting it by hand. So I tried to encode the opposite — a no-AI-slop doctrine — into a Claude Code toolkit. Hard rules, enforced by a hook that scans every file Claude writes and rejects buzzwords before they hit disk: ❌ No lorem ipsum, no [insert headline here], no [your benefit] ❌ No fabricated stats or testimonials. Every quote needs an attribution ❌ Buzzword blocklist: synergy, leverage, seamless, world-class, cutting-edge, innovative, passionate, dedicated, revolutionary, next-generation, disrupting (50+ total) ❌ No 3-up "passionate / innovative / dedicated" feature grids ✅ Every benefit needs a number, name, or concrete artifact. "Save time on reporting" gets rejected. "Cut your weekly reporting from 4 hours to 15 minutes" ships. ✅ 5-second test: a stranger sees the hero and answers (a) what is this? (b) who is it for? (c) what's next? ✅ One primary CTA per viewport. No conflicting goals. A QA-reviewer agent has block authority over the deploy command. If the page has TODO markers, fake testimonials, or buzzword density above threshold, /ship-it refuses to run. A few things I learned that I didn't expect: 1. The single best design decision was a foundational shared context file. One markdown file at .agents/agency-context.md captures niche, ICP, offer, voice, design tokens. Every skill reads it before asking the operator anything. It's the difference between "tell Claude about your business 30 times" and "tell it once, every artifact respects it forever." I borrowed this idea directly from coreyhaines31/marketingskills, which uses the same pattern for SaaS PMM. 2. Don't vendor framework versions. My first instinct was to ship package.json files for the Astro and Next stacks. That guarantees decay in 60–90 days — Astro 5 → 6, Next 15 → 16, Tailwind 3 → 4. I ripped those out and replaced them with bootstrap.md files documenting the current official scaffolder (npm create astro@latest, npx create-next-app@latest). The agent runs the official init at runtime and applies a small overlay. Always current, never decaying. 3. Conversion frameworks beat freestyle prompting. Encoding StoryBrand 7-part as the default service-agency page structure, Hormozi's Grand-Slam Offer as the pricing-section template, PAS / AIDA for paid-traffic LPs — the agent picks the right framework based on page type. Way better than asking Claude to "write good copy." The whole thing is 6 slash commands, 11 skills, 5 specialist agents, 4 industry overlays (dental as the reference, plus legal / home-services / B2B-consultant), all wired through Claude Code's plugin marketplace. It's MIT-licensed and on GitHub if anyone wants to fork it, steal pieces, or pile on with new industry overlays: github.com/heymusa/agency-out-of-the-box Genuinely curious what you'd add to the buzzword blocklist. The list keeps growing every time I review a page that the agent let through and I think "wait, that one slipped". Top current candidates I'm debating: robust, holistic, empower, unlock, streamline. Where's your line? submitted by /u/Musayyab-Naveed [link] [comments]

GPT Image 2 is an epic game changer! (Image Tutorial)

I'm a user of Leonardo.Ai, and they recently added GPT Image 2 to their repertoire. And boy, is that thing fun! The possibilities are huge, and it will probably take a while to fathom them all... Of all the tools I tried so far, it is one of the smartest, and one that is best at understanding - what kind of image I really want to have. So, here is a little "test run" and a tutorial that shows some of the potential of this AI generator, and the type of workflow one can use. What is the tutorial about? I want to show one of the spiffy skills of GPT Image 2: transforming images, creating a new context, generating something new out of the old. And our mission objective is: taking a character, and putting them into various vintage video game designs of the most diverse genres genepool! For this tutorial, I decided to "recycle" a character I had previously generated for various projects. A "Cyborg Hard Techno DJ" called "DJ AI". Step 1: Here they are: DJ AI First, I transformed them into a 16 bit version of themself, with a little help from my friend (aka this very AI generator). I uploaded the picture as "image reference" to Leonardo, and selected "GPT Image 2" as the AI generator I want to use (Leonardo has more than one generator, of course). Step 2: Now, I put them into various genres: Fighter Game Point and click adventure game Platform game Soace Simulation Game Fishing Simulator I used very simple prompts like this - such as "vintage 90s style space sim video game" or "90s style real time strategy video game". Usually, such simple sentences were already enough! A few times there were a few hiccups - "DJ AI" got lost in transformation and was replaced by a more generic player character sprite. On these occasions, I added the line "the reference image should be present in the final image". And this worked like a charm. Step 3: There is no step 3, because this works so clean and seamlessly, that we are finished already. This was a rather "specific" task for this AI generator. But completely different transformations and compositions are entirely possible too. It does not have to be video game specific! I'll report more on this when I come back from further explorations out there. More Examples: Role playing Game Puzzle Game Platform game (another one) Isometric Game submitted by /u/Low-Entropy [link] [comments]

Why AI is erasing your mental map of your projects

Lately, a concerning pattern is emerging: developers are struggling to maintain a mental map of their own projects. We can recall the logic of a project we hand-coded five years ago, yet the one we built with an LLM last week feels like a blur. You aren't losing your edge—your brain is simply reacting to a drastic shift in how you process information. Here is why relying on LLMs is erasing our mental models: The GPS Effect: before smartphones, you built a spatial map of cities. Today, a GPS gets you there seamlessly—but if the screen turns off, you’re lost. Reading LLM-generated code is a passive activity. It delivers the destination but skips the "route-building" required for long-term memory. The Loss of Micro-Decisions: deep learning requires struggle. When you code line-by-line, you make dozens of micro-decisions: naming variables, choosing loops, catching edge cases. LLMs remove this cognitive friction. Without the frustration and the "eureka!" moments, your brain lacks the "hooks" it needs to store the logic. The Speed Trap: memory needs time to consolidate. When you work at the high velocity of AI, your brain lacks the "cool-down" period to archive logic. Memories of the project overlap, blur, and eventually overwrite each other. The bottom line: architecture requires Intimacy The narrative that we can "just focus on the big picture" is a trap. Good architecture requires an intimate understanding of the materials. If you externalize all the implementation to AI, your high-level architecture inevitably becomes brittle. We cannot be "pure architects" if we no longer understand how the bricks are laid. submitted by /u/ApprehensiveAnakin [link] [comments]

The Missing Layer In AI

AI today has mastered context — but it’s still blind to time. That’s a problem. If a user returns after 2 hours or after 3 days, the system behaves the same: it resumes as if nothing changed. Technically smooth, but behaviorally off. Because in reality, time reshapes everything — intent, priorities, focus, even emotional state. A short gap signals continuity. A longer gap demands context recovery. A very long gap requires intent revalidation. Yet current conversational systems treat all gaps equally. This is the missing layer: time-aware AI. Time awareness enables systems to adapt interaction patterns dynamically: - Short gaps → seamless continuation - Medium gaps → structured recap - Long gaps → intent check and re-alignment From a product and business perspective, this isn’t a minor UX tweak but it fundamentally impacts engagement loops, retention, workflow continuity, and habit formation. We’ve optimized for context-aware AI. The next frontier is time-aware AI — systems that don’t just remember what was said, but understand when it matters. submitted by /u/Ninja_BeameR [link] [comments]

Switching between AI experiences

I'm wondering how many people here switch between ChatGPT, Claude, and other AI experiences? I've found it really annoying that I can't seamlessly take my personalization with me between them but find each good at various things ... Also when I'm on a site that has an ai driven experience like support or a travel planner I have to reestablish by identity to get a useful output. I've been wondering if a good way to solve this is a centralized identity layer which works with MCP to connect to any agent - here's my stab at starting this: [https://www.mypersonalcontext.com/\](https://www.mypersonalcontext.com/) Would love to know if this problem resonates with others here and how acute it actually is? Could you see yourself using something like this to make model / agent switching easier? submitted by /u/PNWHygge [link] [comments]

I built a Karpathy-inspired autoresearch plugin for everyday software work in Codex

I built Codex Autoresearch, a Codex plugin for people who are tired of asking an AI agent to "make this better" and getting back a confident little pile of vibes. Karpathy's autoresearch made a very simple thing click for me: AI agents should not just "try to improve things." They should run experiments, measure the result, and preserve the evidence. More importantly: they should make the entire experience of software & infrastructure optimization as seamless as just talking to your agent. So I built a Codex plugin around that idea. Repo: https://github.com/TheGreenCedar/codex-autoresearch Codex should not just make a change and narrate bravery. It should run the benchmark, read the metric, decide whether the change earned its place, remember what happened, and keep going. Feedback is welcome! submitted by /u/aelgorn [link] [comments]

Open-sourced 11 Claude skills for SEO: page audits, content briefs, article writing, no setup

Just open-sourced the SEO skill pack we use in production at InhouseSEO. 11 Claude skills, all opinionated: semantic networks over keyword density, demonstrated E-E-A-T over author bios, information gain over word count, anti-AI-slop writing rules baked into the prompts from the start. The 11 skills: page-audit: 7-dimension audit covering information gain, semantic depth (entity/predicate/EAV analysis, Koray Tuğberk GÜBÜR style), E-E-A-T weighted toward demonstrated experience, structure/time-to-value, on-page technical prioritized by Kyle Roof's POP test hierarchy (title > body > URL > H1 > H2 > alt text), engagement & Discover readiness, conversion. content-brief: SERP gap analysis, not keyword density targets. Maps the specific entities, subtopics, and PAA questions the top 3 competitors cover that your piece doesn't. write-content: full article writing with the anti-slop ruleset (see below) plus content-type structure matching (PAS for service pages, inverted pyramid for definitions, AIDA for comparisons). improve-content: rewrites an existing page using the same rules. keyword-deep-dive: intent classification, SERP volatility reading, CTR benchmarks (First Page Sage 2026 data), 90-day ranking plan. semantic-gap-analysis: lists the specific entities, predicates, and Entity-Attribute-Value relationships the top 3 ranking pages have that yours doesn't, classified by importance (core gap, differentiator, commodity, opportunity). eeat-audit: scores Experience/Expertise/Authoritativeness/ Trustworthiness on what's demonstrated in the content, not declared in the bio. The Experience dimension catches most AI-written content cold. topic-cluster-planning: hub/spoke architecture with publishing order (spokes first, hub second, prevents orphan-hub launch). featured-snippet-optimizer: format-matches by query type (what is → paragraph, how to → ordered list, X vs Y → table) and rewrites the answer block. linkbuilding: phase-appropriate tactics (foundation / growth / authority) from 9 playbooks with real conversion rates and anchor text safety check. expert-interview: extracts first-party knowledge through targeted questions, feeds into write-content. The anti-AI-slop ruleset is the part I want feedback on most. It's not a full "humanizer" or trying to build a human character. What it does: Tiered banned vocabulary (delve, leverage, landscape, seamless, furthermore, moreover, pivotal, robust, harness, showcase, culminate, spearhead) baked into the prompt from the start Banned phrases ("It's worth noting", "In today's [anything]", "plays a crucial role", "In the realm of") Structural tell detection: rule-of-three groupings, synonym cycling, em-dash chains (max 1-2 per 1000 words), copula avoidance ("serves as" → "is"), participial tack-ons, uniform sentence burstiness flagged when 3+ consecutive sentences are within 3 words of each other Horoscope Test per paragraph: could this have been written for anyone, about anything? If yes, inject specific knowledge The 30% Rule: at least 30% of any article must contain details no generic AI could produce Researched and inspired by Wikipedia's AI-cleanup project, StyloAI's stylometric markers, blader/humanizer, and conorbronsdon/avoid-ai-writing. Also in the repo: 25 content-type templates (how-to, comparison, pillar, listicle, pricing, integration, location, programmatic, case study, alternatives, product review, buying guide, etc.) with exact H1/H2 structure, schema markup, featured snippet format, and word count targets per type. 9 link-building tactic playbooks with search operators and real conversion rates. Install for Claude Code: `git clone https://github.com/inhouseseo/superseo-skills ~/.claude/skills/superseo-skills` Claude Desktop: paste any file from /skills into a Project's custom instructions. Each file is self-contained. https://github.com/inhouseseo/superseo-skills Curious what people think of the anti-slop ruleset specifically. Particularly whether the structural tell detection (burstiness checks, Horoscope Test, 30% Rule) actually survives across different models and agent setups. submitted by /u/Equal-Rough-7547 [link] [comments]

I wrote a bespoke code review tool with domain context a first-class feature

Domain-rich code review tool I would consider myself an AI power user, and have been for a while. My (working) world revolves around Claude Code somewhat. The stuff I use it for is pretty impactful financially (algotrading on Polymarket and elsewhere), so I need to ensure that what it does is always on-point. I had a scare yesterday with a bug in my virtually 100%-AI-codebase that could have been extremely costly if I didn't spot the symptoms and luckily I was awake for it. Like many here, I feel that I've suffered from a drag in reasoning effort in recent days even though I always use `/effort max`. A consequence is that I feel quality across the board has dipped, I need to have more oversight, and subtle bugs creep in, especially in a highly complex codebase which I effectively didn't write (I wouldn't have had the time to write it myself anyway; AI really is I would estimate a 10x multiplier for me in many cases, as a SWE with 10 years of experience). One major gripe is that it's difficult to get to the crux of issues, especially really tricky race-condition-like bugs. Even more difficult to validate that a solution is sufficient and necessary. AI can produce an essay explaining its reasoning, but often it's in a poor format for review or too verbose, and many times it does have flaws. I sometimes ask it to produce rich interactive websites or visualizations to help me fully understand what it's talking about, and take (unit) testing to the next level with interactive scenario testing using mocked or real data. I imagine the next major step in AI (e.g. Mythos Preview level or beyond) can change things regarding being more autonomous and trustworthy, but we're not there yet. I was 'wasting' time trying to wrap my head around this one particular bug which needed to go through several rounds of revisions (many hours) as AI couldn't resolve it adequately. Its final solution looked promising but it was still difficult for me to fully conceptualize it, and I work well off of visuals. I decided to get it to code up a generic code review tool, ingrained in domain-knowledge-awareness. I intend to use this more going forwards. I think it marries up well for me the static/bland nature of reviewing code in an IDE or GitHub (with e.g. Copilot reviewer) with the domain-level-expert nature these agents are supposed to embody. It took me less than an hour to get this tool to where I needed it from start to finish. Around as long as it took me to write this post to share with you what's possible. I'm excited for the future of AI, but daunted by my inability to utilize it to the extent that I would like to. Full prompt below, however screenshots I sent it are omitted. In original format with warts and all so you can see shortcomings of both human (me) and AI. EDIT: To show you more about the visualization aspect and how I want to stretch capabilities beyond simply pretty formatting of Claude's reasoning, here are more interesting annotations it made without me prompting for them (after all it's supposed to be tasked with explaining itself to me as per my requests in the prompt below): Interactive visualizations Can we create a new tool. you can put this in the parent of the Polymarket folder. i.e. the git repo dir github. a tool which is specifically for code reviews. think of this like an interactive git differ. something better and more informative than github diff or github desktop diff or vs code git diff. I want something more powerful. so you still do the diff visualisations and everything and be able to navigate a large git change over many files and for files which span many LOC, with seamless performant ease. but at least one killer feature: I want you as the AI who wrote a code change for my review to basically annotate the diff with rich info and visualisation (as needed) explaining fully various aspects of this code change. think basically a UI/UX and workflow that is no less powerful and impactful than the feature of Copilot writing comments in a PR in GitHub (since comments can contain rich text and images and so on as well and are obviously often attached to specific parts of the code change for direct reference)... but I think we can do better. that way I have a best in class tool for reviewing code rather than reading your prose above and tediously switching back and forth between tabs trying to make sense of everything. so I need this as a first class tool which opens as a website. you would generate code changes for my review as they occur: I will prompt the AI by saying, ok now translate this code change and commentary into a format that our tool supports so I can view it in the website. once you're done with this: and you can spawn an army of specialist agents to build this for you... just orchestrate it expertly... obviously use your code change and commentary above as the first use of this new tool so that I can review it with amazing ability --- I think in this tool, which should live in

Cross-Agent AI Workspace: Seamless Transition From One Agent to Another

I just began building a full AI workspace and I got frustrated when Claude went down one day and I lost context on what I was working on. So I built a system where it doesn't matter which AI I use — they all share the same workspace. If Claude goes down or I run out of tokens, I just switch to Gemini and keep going like nothing happened. Here's how it works: I have one folder on my PC. Inside is a master context file that tells any AI agent who I am, what businesses I run, how files should be named, and where everything lives. Each agent gets a bridge file that points back to this master. Session logs act as the handoff — one agent writes what it did, the next reads it and continues. Claude and Gemini are my two agents right now. Both have filesystem access. Both follow the same rules. Every file they create has an agent code (CLD or GMN) so I always know who made it. It's not perfect — I'm still the orchestrator, and nothing runs autonomously yet. But the cross-platform continuity is exactly what I wanted. No lock-in, no lost context, and I can add more agents anytime. Curious if anyone's built something similar. What would you improve? Note: Wording and grammar enhanced with AI for clarity, but this is exactly what I mean. submitted by /u/kebilane [link] [comments]