Category Focus/categories/code-assistant
Code Assistant AI Tools
Discover and compare the best code assistant AI tools and software. Browse 81+ curated tools with reviews and rankings.
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Grok 4.7 is SpaceXAI's most powerful model for coding and knowledge work, and the company describes it as its most capable model for these tasks. According to the announcement, Grok 4.7 works longer on difficult tasks, checks its own work more carefully, and comes with SpaceXAI's best-calibrated safeguards to date. It is served at the same price and speed as Grok 4.6, which the company says makes it highly competitive in its class, and it is pitched as twice as fast at half the price of comparable models. Grok 4.7 is available today in Cursor and Grok Build, through the Grok API, and across third-party coding harnesses, model routers, and cloud platforms. The announcement frames Grok 4.7 around the demands of long, difficult work rather than short chat interactions. SpaceXAI states that the model uses a new, larger base model compared to Grok 4.6 and that it was trained with a longer reinforcement learning run on a harder mix of tasks, weighted toward problems that take many hours to complete. This focus matters because long-running coding and knowledge tasks place unusual pressure on a model: it must stay coherent across extended context, avoid drifting from the original objective, and catch its own mistakes before handing work back. The company reports that Grok 4.7 is better at verifying its own work and managing longer context, which directly targets those failure modes. On CursorBench 4.0, a benchmark that stresses longer-running coding tasks, SpaceXAI says Grok 4.7 sits at the frontier in price-performance, with a chart comparing it to Fable 5.1, Opus 5, GPT-5.6 Sol, and Sonnet 5 on benchmark score against average cost per task. Beyond raw size and training duration, SpaceXAI highlights a specific capability addition: Grok 4.7 was trained to natively understand the Grok Bot harness. The company states this makes the model better at conversational tasks and general knowledge work. In practical terms, a model that natively understands its harness can operate more naturally inside that environment, handling multi-step conversations and everyday knowledge tasks without the friction that comes from adapting an externally trained model to a new interface. That combination, a larger base model, a longer reinforcement learning run weighted toward multi-hour problems, improved self-verification, better long-context management, and native harness understanding, is the core of what distinguishes Grok 4.7 from its predecessor. SpaceXAI publishes a detailed benchmark table comparing Grok 4.7 against Grok 4.6, GPT-5.6 Sol, and Fable 5.1. Grok 4.7 scores 46.3% on CursorBench 4.0, 71.0% on DeepSWE v1.1 with a high-effort marker, 64.0% on EEBench, 1,657 on AA Briefcase v1.1, 37.6% on Terminal-Bench 4.0, 19.6% on the Harvey Legal Agent Benchmark, and 56.7% on HealthBench Professional. The company also reports a GDPval Elo score of 1,695 for Grok 4.7 at xhigh effort, against 1,735 for Fable 5.1 at max, 1,605 for Grok 4.6 at high, and 1,542 for GPT-6 Astra at max. These benchmarks span software engineering, multi-hour terminal work, multi-hour office work, electrical engineering, legal work, and clinical reasoning, which reflects the breadth of tasks the model is positioned to handle. On professional knowledge work, SpaceXAI states that Grok 4.7 is better at creating documents and presentations. In GDPval and AA Briefcase, the company explains, AI is asked to work on tasks done by professionals such as lawyers, nurses, and financial analysts. Grok 4.7 improves upon Grok 4.6 on both benchmarks and performs comparably to other frontier models. That means the model is not positioned only as a coding tool: it is also aimed at the document-heavy, multi-hour office work that these professions perform, where producing a usable deliverable matters more than producing a quick answer. The benchmark table supports this positioning, showing Grok 4.7's AA Briefcase score of 1,657 against 1,546 for Grok 4.6, 1,487 for GPT-5.6 Sol, and 1,678 for Fable 5.1. Safety and cybersecurity are treated as a first-class part of the release. SpaceXAI says Grok 4.7 was built with an entirely new safeguard stack and that it is the strongest model the company has tested on refusals and jailbreak resistance. In dual-use domains such as cybersecurity and biological work, the company reports that it leads on both utility for benign tasks and safe refusal on dangerous ones, topping LatchBio's biosafety benchmark at 62.4%. The company also states that Grok 4.7 balances strong cyber defense capabilities with low refusal rates for legitimate use, showing the highest safety on HackerBench v0.3, its benchmark for risky and malicious cyber tasks, allowing only 3.3% of risky dual-use prompts through while rarely blocking legitimate security work. In addition, SpaceXAI has started giving select cybersecurity partners invite-only access to Grok 4.7's red-team capabilities for defense research. The overall approach behind Grok 4.7 is a combination of scale, extended reinforcement learning, and deliberate alignment work rather than a single headline change. SpaceXAI describes a new, larger base model trained with a longer reinforcement learning run on a harder mix of tasks weighted toward problems that take many hours, which produces a model that verifies its own work and manages longer context more effectively. Native training on the Grok Bot harness adds conversational and general knowledge work strength, and an entirely new safeguard stack supplies refusal and jailbreak resistance alongside dual-use safety. The company then serves the result at the same price and speed as Grok 4.6, with an additional fast variant that runs at twice the output speed for twice the price. Each element reinforces the others: a stronger base model makes longer autonomous runs viable, and a stronger safeguard stack makes those runs safer to deploy. Pricing and availability are explicit. Grok 4.7 is priced starting at $2 per million input tokens and $6 per million output tokens. SpaceXAI also serves a fast variant with twice the output speed at twice the price. The model is available today in Cursor and in Grok Build, and it is also available through the Grok API, third-party coding harnesses, and model routers and cloud platforms. For developers who want to try it before committing, the company offers a free try in Grok Build, and it publishes a terminal install command: curl -fsSL https://x.ai/cli/install.sh | bash. API keys can be created from the console, and documentation is available at docs.x.ai. For users, the stated benefit is straightforward: frontier-class coding and knowledge work at a price-performance point that makes long-running tasks economically practical. Because Grok 4.7 is served at the same price and speed as Grok 4.6, teams that already use the previous model can adopt the new one without changing their cost structure, while gaining a larger base model, longer reinforcement learning, better self-verification, and stronger long-context handling. The improved document and presentation abilities extend that value beyond engineering into professional knowledge work, and the new safeguard stack gives organizations that operate in sensitive or dual-use domains a model that the company says rarely blocks legitimate security work while refusing dangerous requests. Concrete use cases are visible in the benchmarks SpaceXAI selected. Software engineering and longer-running coding tasks are covered by CursorBench 4.0 and DeepSWE v1.1. Multi-hour terminal work is covered by Terminal-Bench 4.0. Multi-hour office work, producing documents and presentations, is covered by AA Briefcase v1.1 and GDPval. Legal work is measured by the Harvey Legal Agent Benchmark, clinical reasoning by HealthBench Professional, and electrical engineering by EEBench. Cybersecurity is addressed both through the HackerBench v0.3 safety results and through invite-only red-team access for selected cybersecurity partners conducting defense research. Together these scenarios describe an agent that can be pointed at a long task, left to work, and expected to check its own output. The target audience follows from that positioning: software developers and engineering teams building in Cursor, Grok Build, or through the Grok API; organizations running third-party coding harnesses, model routers, and cloud platforms; and professionals whose multi-hour work involves documents, presentations, analysis, and research, such as those in legal, clinical, financial, and engineering roles. SpaceXAI also targets cybersecurity defenders, both through the model's balance of strong cyber defense capability with low refusal rates for legitimate use and through the invite-only red-team access it has begun granting to select partners. Grok 4.7's takeaway is a single proposition: SpaceXAI's most capable model for coding and knowledge work, delivered at the same price and speed as Grok 4.6 and described as twice as fast at half the price of comparable models. It pairs a larger base model and longer reinforcement learning on multi-hour tasks with improved self-verification, better long-context management, native Grok Bot harness understanding, and an entirely new safeguard stack that leads the company's testing on refusals and jailbreak resistance. Available in Cursor, Grok Build, the Grok API, third-party harnesses, model routers, and cloud platforms from $2 per million input tokens and $6 per million output tokens, it is built for teams that need long, difficult work completed reliably and affordably.
Hyrax AI describes itself as the AI architect for your entire codebase, built to turn AI velocity into better software. According to hyrax.dev, it provides architecture, improvements, and governance for AI-native engineering teams, and it continuously understands and improves modern codebases through verified, human-approved changes. In practice, Hyrax maps a repository and understands how a product is built, then identifies what actually matters across the codebase and turns improvements into production-ready changes delivered as GitHub pull requests. The company's FAQ states the goal plainly: help engineering teams understand what is happening across their codebase, identify what matters, and turn improvements into production-ready changes. An engineer reviews and merges every one of them. The problem Hyrax targets is visible in how AI coding tools changed engineering. The Product Hunt description frames it directly: code review tools tell you what is wrong with a pull request, while Hyrax finds what should improve across your entire codebase and does the work. Hyrax's own FAQ draws a distinction between writing code and architecting the system that code enters. AI assistants edit what you point them at, and their context starts empty every session. Hyrax instead holds the map of your codebase, so specialized agents can reason about security, correctness, maintainability, performance, architecture and operations before anything ships. The company's stated position is that teams can use both kinds of tools together. Discovery is where Hyrax begins. The product maps modules, entry points, ownership, and dependencies together so that architectural problems appear in context rather than in isolation. In the interactive demonstration on hyrax.dev, Hyrax discovers a sample repository, acme/storefront-web, reading 38 files and resolving 4 entry points. The architecture map shows three layers: src/api, the HTTP surface and error envelope; src/domain, which holds pricing, cart, and tax rules; and src/lib, primitives with no business rules. Discovery flags a finding of one dependency cycle, where src/domain/tax imports from src/api/types, an inner layer importing outward. The result is an architecture map with a prioritized list of open findings, each carrying an identifier such as HYRAX-402, a severity such as P0, P1, or P2, a plain-language description, and the exact file and line where the issue lives, for example src/lib/env.ts:42 or src/components/SearchResults.tsx:34. From that map, six specialized agents evaluate the codebase across six engineering domains: security, correctness, maintainability, performance, architecture, and operations. Hyrax prioritizes the issues it finds so the highest-leverage work comes first. In the demo, findings range from a P0 hardcoded secret in the environment loader and a P0 PCI DSS issue where a raw PAN is routed through the application backend, to P1 items such as a session token stored in localStorage and exposed to XSS, a missing CI pipeline with dependency vulnerability scanning, and a missing React ErrorBoundary that shows a blank screen on a render exception, down to a P2 array index used as a React key in SearchResults. Each finding is tied to a specific location and to one of the six domains, which is what allows the prioritization to reflect the codebase rather than a generic lint rule set. Hyrax does not stop at reporting. It writes each fix in context, does the work itself, and verifies the result before anything reaches your team. The 13-step verification gate covers isolated worktree execution, the tests it started with, the tests after the change, your build, lint and formatting, a size limit on the diff, a second review by an independent agent, a re-scan to confirm the original issue is gone, and CI. If a required check fails, the work stops and never becomes a pull request. In the demo, a layering violation is fixed by moving a shared type into the domain that owns it: the reverse dependency disappears without widening the scope, 142 tests pass, the production build succeeds, the dependency cycle is removed, and a reviewer agent approves. Hyrax then opens a GitHub pull request, in that example one titled "[Hyrax] Load API_KEY and DATABASE_URL from the environment" that resolves a critical finding where secrets were committed literally in src/lib/env.ts, with the change verified against all checks. Governance and agent access extend the same model. Approved architecture rules live with the repository in a HYRAX.md file. The demo example reads: dependencies point toward src/domain, and shared types live with the domain that owns them. Those approved rules guide future work, which keeps the decision with the repository rather than with the tool. Hyrax also announced Hyrax MCP, which gives Claude Code, Cursor, and Copilot live codebase context. The overall workflow follows the loop shown on the site: discover, audit, fix. Hyrax works through GitHub with human control and never merges on its own; verification runs before a pull request reaches your team, and your engineers make the final call. All inference runs in the Hyrax AWS Bedrock account, and Hyrax does not train on customer code. The outcome Hyrax claims is better software with every change. Teams get visibility into what is happening across the codebase, a prioritized view of what matters, and fixes that arrive as production-ready changes rather than raw suggestions. Because every improvement must pass the verification gate, the work that reaches reviewers has already survived isolated execution, the repository's own lint, typecheck, tests and build, an independent second review, a re-scan confirming the original issue is gone, and CI. The customer proof quoted on the site from Joel Horwitz, CEO of Synter, says: "We pointed Hyrax at Synter's own codebase and it came back with issues we had not caught, each one with a fix ready for review." The demo lists concrete ways the product is used: map a repo, review prioritized improvements, or open a verified pull request. Mapping suits a team that needs modules, entry points, ownership, dependencies, and cycles in one place. Reviewing prioritized improvements fits the discover and audit steps, where findings are ranked by severity and annotated with attributes such as small effort and medium risk, with actions labeled Fix, Implement, View, or Easy win. Opening a verified pull request covers remediation: Hyrax writes the patch, runs the repository's own lint, typecheck, tests and build in an isolated worktree, and opens a PR such as one that loads API_KEY and DATABASE_URL from the environment instead of committing secrets literally. Hyrax MCP supports a related workflow by supplying coding assistants in Claude Code, Cursor, and Copilot with live codebase context. Hyrax is aimed at engineering teams, particularly AI-native engineering teams, with GitHub as the delivery surface and repository-level architecture rules as the control mechanism. Pricing has two plans. Free is $0/mo and includes the full product on real repos with no card required, everything Hyrax does with no feature walls, up to 100 PR reviews a month for free, a $30 starter credit, and $10/month of credits every month. Paid is $30/user/mo and includes everything in Free plus $30/month of credits per user; overage is opt-in with budget caps you set, so Hyrax cannot exceed the cap. Credits meter usage across repository mapping, verified improvements, and PR reviews. Tech details stated on the site include that all AI inference runs on AWS Bedrock and that Hyrax does not train on customer code. Hyrax positions itself as the AI architect rather than another assistant: it holds the map of your codebase, prioritizes improvements across six engineering domains, writes fixes in context, verifies them through a 13-step gate, and delivers them as GitHub pull requests that a human reviews and merges. The value proposition is turning AI velocity into better software, with architecture, improvement, and governance built in.
LucentraCode is an AI coding CLI built for developers who want powerful models without constantly worrying about usage limits or expensive subscriptions. It runs directly from the terminal, letting developers choose from models such as GPT-6 Astra, GPT-5.6 Sol, Claude, Gemini, Grok, Kimi and more, and apply them to real development work — debugging, building features, refactoring, testing, and working across larger codebases. The product frames itself with a simple promise: "code without the clock," a runtime designed to keep developers in flow state instead of making them pause for quota windows to reset. The problem LucentraCode targets is the stop-start rhythm of many AI coding subscriptions. Quota windows expire, sessions are interrupted mid-execution, and the most capable models are frequently locked behind $100–$200 per month tiers. The site positions the product directly against that model. It states its aim as building for flow state with "no 5-hour resets," and highlights that work should not be interrupted by session quota timers or mid-execution lockouts. For developers who work in long, uninterrupted stretches, that reset cycle is more than an inconvenience: it fragments concentration, breaks context, and pushes people toward either paying for a higher tier or switching tools midway through a task. LucentraCode's answer is to remove the countdown clocks from the equation and replace them with a single, predictable allowance. The first pillar of the product is its approach to session limits. LucentraCode advertises a session quota timer that is uncapped, with no 5-hour limit, and zero interruptions caused by mid-execution lockouts. The intended outcome is a continuous flow state that remains guaranteed active throughout long working sessions, so developers are not forced to stop and wait for a quota window to reset. The site presents this as an always-on capability rather than a best-effort behaviour, contrasting it with the countdown clocks that characterise many competing coding assistants. In practice that means a developer can start a refactor, a debugging session, or a feature build and carry it through to completion without watching a timer or breaking their working context to check remaining quota. Long sessions become a normal way to work rather than something to ration. Second, LucentraCode brings frontier models within reach on a much lower tier. The site states that premium models such as GPT-6 Astra are available without jumping straight to a $100–$200 tier, and it contrasts that competitor requirement with its own $20 per month plan that includes all models. The model line-up shown on the site includes GPT-6 Astra, Claude 3.7 Sonnet, Opus 5, GLM 5.3, and Fable 5.1. To make a single monthly allowance stretch further, the product uses Smart Auto, which picks the right model for the job: cheaper models handle search, tests, and routine work, while stronger models handle implementation, architecture, and review. That division of labour is the mechanism behind the allowance efficiency the site claims — 3x–5x more code shipped from the same pool of usage. Third, usage is organised around one simple monthly pool. LucentraCode eliminates rolling timers and windows entirely — the site counts them as zero, describing them as eliminated — and instead uses a single unified balance presented as an account meter. The stated benefit is usage freedom: developers spend their allowance when they actually need it, inside a single monthly allowance that the site says involves zero artificial lockouts. Rather than juggling multiple reset timers and separate meters for different models or workloads, the developer has one balance to monitor, which makes both planning and day-to-day work simpler. The design philosophy is that the meter should reflect real work done, not an artificial schedule imposed by the provider. Getting started follows a short, terminal-native workflow. LucentraCode is installed globally with a single command, `npm install -g lucentracode`, and is then launched by running `lucentracode`. It requires Node.js v20 or later. The runtime is available on Linux, Windows, and macOS, so the same command-line workflow carries across the major desktop operating systems. Once launched, the terminal is where model selection, routing, and real development work take place — the product is built to be used inside the environment where many developers already spend their day, rather than through a separate IDE plugin or web application. There is no separate application to keep open or context to switch into; the CLI is the product. The benefits LucentraCode promises are practical rather than abstract. Uninterrupted sessions support deeper focus and preserve context across long tasks. A single monthly pool removes the arithmetic of multiple reset windows and separate balances. Smart Auto routing means the most expensive reasoning is reserved for the work that genuinely needs it, so routine tasks never consume premium capacity unnecessarily. Access to frontier models at a $20 entry point lowers the cost of working with the strongest available models, and the site summarises the combined effect as shipping 3x–5x more code within the same allowance. For teams, that translates into fewer decisions forced by quota mechanics and more decisions driven by the task at hand. LucentraCode is described as a tool for real development work, and the listed scenarios span the full development loop. They include debugging, building features, refactoring, testing, and working across larger codebases. The Smart Auto routing maps naturally onto that spread of work: search, tests, and routine tasks are handled by fast, efficient models, while implementation, architecture, and review are handled by frontier reasoning models. A developer working in a large codebase can therefore use the runtime for exploratory search and routine test generation, escalate to stronger models when implementing a substantial feature or reviewing an architectural decision, and keep the entire workflow inside a single terminal session. Because there is no 5-hour reset in play, a long debugging investigation or a multi-hour feature build does not have to be paused and resumed around a quota window. LucentraCode is aimed at developers and serious builders — people who want frontier models for everyday coding work without premium-tier pricing. Pricing is organised into named runtimes. The Operator plan costs $20 per month and offers more than 5X the usage of the ordinary plan, with models including everything available in Ordinary, plus claude-opus-5, claude-opus-4.8, and gpt-6-astra. The Obsessed plan costs $40 per month, gives 2.5X more usage than the Operator plan, and includes everything in Operator plus claude-fable-5.1 and claude-fable-5. The Ordinary entry tier is available in India only, and international pricing is displayed in USD and INR. Full plan details are available through the LucentraCode dashboard. The site notes that the pricing shown is a preview and that availability and billing details will be announced at launch. Taken together, LucentraCode's proposition is straightforward: keep the terminal workflow developers already use, give them a broad set of frontier models through a single monthly allowance, and remove the countdown clocks that interrupt long sessions. Smart Auto routing makes that allowance go further by matching model strength to task difficulty, and a $20 entry point brings premium models within reach of individual developers rather than only teams that can justify a $100–$200 monthly tier. For developers who judge a coding assistant by how little it interrupts them, that combination — one pool, many models, and no reset timer — is the core value proposition.
Bolt Forge is a new agent inside Bolt.new, the AI app builder, that runs on open-source AI models only. It launched on September 14, 2026 as a research preview and sits in the agent picker next to the Standard and Max agents that Bolt users already work with. Forge is an agent rather than a single model, so builders can switch into it, build with open models, and switch back to Standard or Max at any time. Its headline promise is scale of usage: every individual Pro plan includes up to 50X more Forge usage at no extra cost through October 14, 2026, with no daily caps. In exchange, builders who opt in share de-identified build sessions that help train new open models, starting with the U.S. open-model lab Arcee AI. Bolt Forge was created because price has decided who gets to build with AI at speed and scale. Builders arrive at Bolt with an idea and the skill to see it through, then ration prompts like fuel: every brainstorm, every rough draft, every dead end burns credits priced for polished work. The drafting phase of building, the part where you need room to wander, is exactly the part usage caps punish. Forge removes that penalty by making the allocation big enough to draft, test, tear down, and rebuild, so builders can test a big idea, play around with new concepts, and experiment as far outside the box as they want, while saving premium credits for the work that needs them. A second motivation is the models themselves: Bolt states that AI inference costs have dropped 280x in 18 months according to the Stanford HAI AI Index 2025, a collapse that makes an allocation this large possible, and open-source models improve when they see how real software gets built, which is the one thing you cannot scrape. Opted-in Forge sessions provide exactly that, with consent. Forge runs a lineup of open models that users can see and select. As of September 2026 that means GLM 5.3 Flash with GLM 5.3 alongside it, plus Kimi K3 and DeepSeek v4 Pro as experimental options. The lineup will evolve, and when a new open model drops, Forge is the first place in Bolt it lands. Two honest notes accompany the lineup: these models are experimental inside Bolt, so builders should duplicate their project before switching a serious build into Forge and keep complex production work in Standard or Max; and they do not all cost the same to run, because Kimi K3 and DeepSeek v4 Pro burn through usage faster than the GLM pair, so starting on the default and reaching for the heavier models when the work calls for it is the recommended approach. Forge also cannot take PDF uploads yet. Capability was tested before shipping: the Forge lineup ran through the Bolt Build Index, Bolt's own benchmark for how well a model completes real Bolt projects, and came out at 92.2 against 101.0 for the top paid model, Claude Opus 5, which is 91% of the top score. The Forge allocation is a headline part of the offer. Every individual Pro plan includes it at no extra cost through October 14, drawn from a single monthly bar that resets on the renewal date, with no daily limits and a hard stop at 100%. Every Pro tier gets the same allowance, and Forge usage is separate from Standard and Max usage. When the bar hits 100%, Bolt switches the builder back to Standard rather than charging an overage, so usage can be read at a glance with no daily math and no surprise pause mid-project. Bolt describes the allocation itself as the payment for the data builders choose to share, and says the amount is big enough to draft, test, tear down, and rebuild. Training in Forge is opt-in. A one-tap consent screen spells out the trade in plain language before a builder begins working in Forge. The shared data includes prompts, code, project files and configuration, the tool calls Bolt makes, and edit histories including the fix traces Bolt creates. Bolt strips and de-identifies secrets and personal information before anything leaves its infrastructure, and validates the pipeline against seeded test data. Switching back to Standard or Max stops Forge from collecting anything new, and builders can email privacy@stackblitz.com to ask Bolt to stop using Forge content it has already collected. Teams and Enterprise workspaces are excluded from Forge and from AI training and dataset licensing, and sessions from the EEA, the UK, and Switzerland are not used for training or datasets either, so builders in those regions get the Forge allocation without the trade. The training side starts with Arcee AI, the U.S. open-model lab behind the Apache 2.0-licensed Trinity model family; Bolt has partnered with Arcee to help train a trillion-parameter-class model, and sessions shared during the preview window from September 14 to October 14, 2026 feed the first training run, which begins in October. A data license agreement governs every transfer, StackBlitz may be paid for the datasets it licenses, and those datasets may go to other AI developers as well as Arcee. Forge works in five steps from start to finish. First, pick Forge in the agent picker, where it appears as a third agent next to Standard and Max on every individual Pro plan. Second, opt in with one tap after a consent screen spells out the trade. Third, build: Forge usage draws from one monthly bar with no daily limits and a hard stop at 100%. Fourth, Bolt de-identifies shared sessions before anything leaves its infrastructure, stripping secrets, sensitive data, and personal information as a rule and validating the pipeline against seeded test data. Fifth, the sessions go into datasets that Bolt licenses to AI developers under a data license agreement, with Arcee AI as the first. Underneath, two pieces of infrastructure make the economics work. Bolt runs Forge on its own reserved hardware instead of paying a provider per request, so a fixed, predictable cost means more of what you pay goes to building instead of markup. And Forge projects run in the browser on WebContainers, the technology StackBlitz built and Bolt runs on, so there is no server rented for every build, builds stay fast, and costs stay low. Benefits of Forge centre on removing the competition between experimentation and production work. Brainstorms, MVPs, and experiments stop competing with production work for premium credits. Users get 91% of the top paid model's Bolt Build Index score included with Pro at no extra cost. Usage is readable at a glance: one monthly bar, no daily limits, and a hard stop instead of an overage bill. And builders get a hand in what comes next, because their sessions teach open models how real software actually gets built. Bolt frames it simply: every Forge build does two jobs, it ships your thing and it teaches open models how real software gets built. Already on Pro, there is nothing to buy and no line to stand in. In practice, Forge is designed for the drafting phase of building. It is the place to test a big idea, play around with new concepts, and experiment far outside the box, with enough room to draft, test, tear down, and rebuild without rationing prompts. Because the allocation is separate from Standard and Max usage and has no daily limits, builders can run brainstorms, MVPs, and experiments in Forge while keeping premium credits for production work. Users who want to try the heavier experimental models such as Kimi K3 and DeepSeek v4 Pro can do so when the work calls for it, while starting on the default GLM pair for everyday building. Duplicating a project before switching a serious build into Forge is the recommended workflow, given that the models are experimental inside Bolt. Forge is aimed at individual Pro plan builders on Bolt.new: people who arrive with an idea and the skill to see it through, and who want room to experiment without burning premium credits. Teams and Enterprise workspaces are excluded from Forge, and from AI training and dataset licensing, and sessions from the EEA, the UK, and Switzerland are not used for training or datasets. Getting started on an individual Pro plan means opening the agent picker, choosing Bolt Forge, and taking the one-tap opt-in; Bolt says you are building on open models in under a minute. Builders who are not on Pro have two doors: join the Bolt Lite waitlist, where codes go out in waves and the first seats open by September 21, 2026, or skip the line with Pro at $25 a month billed yearly, which includes up to 50X Forge usage at no extra cost and no access code required. Bolt Lite is offered at $9 a month, and anyone on Bolt Lite when sign-ups close on October 14, 2026 keeps the plan. Bolt Forge is Bolt.new's experiment in making open-source models the everyday building surface. It trades up to 50X more usage, no daily caps, and a dedicated allocation for opt-in, de-identified build sessions that help train open models with Arcee AI. The trade-off against Bolt's top paid model is nine index points; the payoff is room to draft, test, tear down, and rebuild without rationing prompts, plus a hand in what comes next for open models.
Devin Voice is the voice mode built into Devin, Cognition's AI software engineer. It lets you talk naturally with Devin to explore ideas, pressure-test an approach, and hand off work while you are away from your keyboard. Rather than typing every instruction, you start a voice call, speak your thoughts out loud, and let the conversation move at the speed of speech. Any message you have already typed is sent when you start the call, so you can move directly from a written prompt into a spoken discussion inside the same session. The Product Hunt listing describes the same idea more bluntly: you say it, Devin ships it, and you speak a task out loud while Devin plans, codes, and delivers. The capability is aimed at people who already work with Devin in Agent mode or in an existing session and want a conversational way to think through problems, ask questions, and keep work moving. Software work has long been keyboard-centric: you type a prompt, wait for a response, read it, and type again. Voice mode changes that rhythm by making the spoken conversation itself the interface between you and the agent. The documentation frames voice mode around three activities: exploring ideas, pressure-testing an approach, and handing off work while you are away from your keyboard. The stated tips reinforce how this is meant to feel in practice. You should not be afraid to interrupt Devin's work, and you should ask questions and clarify your thoughts as you have them. You are also free to interrupt Devin while it is talking. Together, those instructions describe a working style where clarification is welcome at any moment rather than something you have to schedule between long silences, and where getting your thinking out loud is part of the process rather than a disruption to it. Getting into voice mode is deliberately simple. On the home page in Agent mode, or inside an existing session, you click the voice call button that sits beside the message box and then allow microphone access. Hovering over the waveform icon shows a "Start voice call" tooltip, so the control is discoverable before you commit to a call. One detail worth noting: any message you have already typed is sent when you start the call. That means a half-written prompt or a queued instruction is not lost; it is delivered as the call begins, so your spoken conversation continues from the written context you had already built up. Starting from either the home page or an in-progress session means you can begin a call at the moment an idea strikes rather than having to set up something new first. Once a call is running, the documentation lists a small, clear set of controls. Mute microphone pauses your microphone, and clicking Unmute microphone lets you speak again. If you are muted but still want to say something without leaving the call, you can hold Space to talk while muted when you are not typing. Silence Devin turns off Devin's audio without muting your own microphone, and clicking Unsilence Devin brings the audio back. End voice call hangs up. These controls separate the two directions of the conversation, your input and Devin's output, so you can mute yourself while listening to a long explanation, or silence Devin's audio while keeping your own microphone live and ready to respond. Voice mode is not a separate, isolated room. You can navigate within Devin while the call stays connected, so you can move around the product without dropping the conversation. Your conversation appears in the session history, which means the spoken exchange becomes part of the recorded session rather than disappearing when you hang up. The documentation also notes that you can shape how Devin speaks: if you have preferences for how Devin should speak, for example to speak faster or slower, or a particular communication style, you can simply ask. There is no described settings panel for this; the adjustment happens through the conversation itself, which keeps the interaction consistent with the rest of the voice experience. Under the hood, the Product Hunt listing states that Devin Voice is powered by GPT-Live for natural conversation, with Cognition's new SWE-2 coding model under the hood. That combination is what the listing describes as letting Devin plan, code, and deliver after you speak a task out loud. Devin Voice connects you to Devin, described in the listing as Cognition's AI software engineer. On the documentation side, the overall description of how the feature works is straightforward: you talk naturally with Devin, in Agent mode or in an existing session, and the conversation is tied into the same session context, appearing in session history and continuing even as you navigate within Devin. The documentation also carries a standard note that responses are generated using AI and may contain mistakes. The benefits follow directly from those mechanics. Voice mode lets you explore ideas out loud instead of composing them in a text box, which the documentation positions as a way to pressure-test an approach. It lets you hand off work while you are away from your keyboard, so time spent away from a desk does not have to mean the work stops. Because you can interrupt Devin's work and ask questions as they occur to you, clarifications do not have to wait for a complete response, and because you can interrupt Devin while it is talking, you are not locked into listening to everything before you can steer the conversation. And because you can ask Devin to speak faster, slower, or in a different communication style, the spoken interaction can be tuned to your preferences. Concrete use cases flow from the documented behaviour. You might start a call on the home page in Agent mode, with a task already typed into the message box, and have that message sent as the call begins so you can talk through the task instead of typing more. You might be inside an existing session and open a voice call there to hand off work while you step away from your keyboard. You might keep the call connected while navigating within Devin, moving around the product without breaking the conversation. You might mute your microphone while Devin talks, or hold Space to talk while muted when you are not typing. You might silence Devin's audio without muting your own microphone so you can think or speak without the audio running. And afterwards, you can revisit the conversation in the session history. In terms of audience and context, the documentation is written for people using Devin itself, referring to the home page in Agent mode and to existing sessions, and describing the voice call button beside the message box. Product Hunt lists Devin Voice under Productivity, Developer Tools, and Artificial Intelligence, and the listing points readers to devin.ai to try Devin. The named technologies associated with the product are GPT-Live for natural conversation and Cognition's SWE-2 coding model under the hood. The documentation page does not describe pricing, plans, or platform availability beyond the described interface, and the listing does not state pricing either. The takeaway is straightforward: Devin Voice turns talking to Devin into a first-class way of working. You say it, and Devin ships it. By letting you start a call from the message box in Agent mode or an existing session, carry a typed message into the call, mute or silence either side of the conversation, keep working while Devin navigates alongside you, and simply ask for the speech style you prefer, voice mode makes it possible to explore ideas, pressure-test an approach, and hand off work away from your keyboard, with the conversation preserved in the session history.
Modeinspect is a design canvas with your codebase and agents built in, positioned as a production-grade AI design tool that runs in your codebase. It is built for design engineers and for teams that want to design high-fidelity product features directly on the real thing rather than in a separate mockup tool. Teams connect their codebase, open an existing screen, and design with their own components, tokens, live data, states, and breakpoints. The canvas is deliberately framed as a means to an end: as the company puts it, the canvas is not the destination, the product is. Design on a real canvas that sits on top of the real product, with all the freedom of a design tool and none of the throwaway mockups. The problem Modeinspect addresses is stated plainly: most software is designed twice, a picture first and then again in code, with intent drifting in between. The traditional path runs through what the product calls the handoff chain, where design passes work down a chain and then waits for it to come back. Mockups are rebuilt in Figma away from the real product, specs and redlines document every state, engineers reinterpret the design in code, and every change restarts the whole loop. Modeinspect describes that old way as taking 45+ days from design to ship, and contrasts it with a canvas-to-PR loop it says runs about 10 days — roughly 4.5× faster. The argument is that design should not be detached from the thing it is designing, because the moment intent is copied into a picture, it begins to drift. The core promise is unified, collaborative design in code. Everything is in one place: your codebase, the canvas, and coding agents are integrated out of the box, with no MCP servers, no localhost, and no devops glue required. A live product can be pulled onto the canvas, letting you capture any element of your live product, pixel perfect and fully editable, so work starts from where things actually are. Rather than prompting for every adjustment, Modeinspect emphasizes controls, not prompts: a padding change should not take a paragraph, so you edit anything, on canvas or in code, with the visual controls you already know. Your changes then build back as canvas to code in one shot, producing clean, scoped diffs with your design system enforced. The canvas is also built for collaboration, with no localhost to share and no branches to wrangle, so you can send a link, collect comments, and open the PR in one click. Modeinspect is built for design engineers, and its component story is deliberately literal. Components are 1:1: you drop in the actual components your product ships, with every variant and every state intact, rather than a redrawn look-alike that quietly drifts from the real thing. Tokens are enforced, so every color, space, and text style comes straight from your library, and everything you place is automatically on-brand — nothing off-system can sneak in. Breakpoints are native: mobile, tablet, and desktop are laid out side by side and each one reflows live, instead of relying on one frozen frame you just hope survives on a phone. And capture to canvas means that when you spot something in the real product you want to rework, you can pull it straight onto the canvas pixel-exact and fully live, and start from where things actually are rather than from an approximation. Dynamic states and real data are treated as first-class parts of the design rather than afterthoughts. Hover, focus, error, empty, loading, and success states are shaped on the real component, so a design never falls apart the moment someone actually uses it. Real data and real flows mean designing on top of live data and genuine journeys — long names, empty states, and the messy edge cases — so your work holds up in the wild and not just in a tidy mockup. AI exploration uses the latest AI models to explore variants, restyle a section, adjust copy, or apply a design direction while you stay in control, which keeps the AI in a supporting role instead of taking over. And because every move you make on the canvas becomes the real product as you make it, there are no redlines, no spec docs, and no waiting on a rebuild: what you design is what ships. A large part of the product's approach is the quality of the code that comes out the other side. Mode reads your file layout, components, tokens, conventions, and existing logic, then writes within them, so PRs land scoped, type-safe, and ready for engineering review. Diffs are scoped and clean, letting engineers review focused changes rather than rewritten surface area or noisy AI churn. Your design system is enforced throughout: Mode pulls from your component library and design tokens, with no hardcoded colors, no magic numbers, and no throwaway components. There is no generated UI debt, because changes reuse your components, tokens, utilities, and styling system instead of creating a parallel design system. And changes are type-safe, with props, state, events, and data shape checked against the product instead of guessed from a mockup. This is the methodology that separates Modeinspect from AI app builders that generate new surface area alongside the one you already maintain. The stated benefits are framed as measurable rather than aspirational. Modeinspect says production-grade is not a tagline but the metric, and it highlights one customer story in which a team merged design and engineering into the same loop, saving 22 days on the delivery cycle, with zero engineering handoffs and design QA removed. Prelude's Chief Product & Design Officer, Quentin Le Bras, is quoted saying his designers explore on the actual codebase, with real data, and open the PR themselves, going from idea to a merged PR without a handoff in between. A Product Design Manager at Kiwi.com describes the tool integrating seamlessly with their codebase and design system, which is exactly what they had been looking for in AI design tools, enabling iteration on top of an already complex product. A Principal Product Manager at Moss calls it the first AI tool that respects their design system 1:1, allowing the team to create production-like prototypes and making the whole team faster. A UX Designer at NCCER highlights the ability to make changes in real time using their design system and immediately push those changes to code for senior developers to review and merge. The product organizes this into three workflows that share one production loop, all inside your real codebase at production fidelity. Prototyping covers prototypes that feel like the product, built with real data, dynamic states, breakpoints, and interactions — so instead of pitching with mockups, teams pitch with the thing itself. Design QA happens all in one loop: compare canvas to live build pixel-by-pixel, spot drift, fix it, and keep moving, with no round-trips through Figma. Shipping PRs covers pushing minor visual changes or new components as merge-ready PRs, with context, screenshots, and a clean diff. Concrete scenarios follow from these: reworking a screen you spotted in production by capturing it to the canvas, exploring variant directions with AI while keeping control of the final decision, validating a layout against long names and empty states using live data, checking a live build against the canvas for visual drift, and sending a link to stakeholders for comments before opening the pull request. Modeinspect is aimed at design engineers and at teams where design and engineering work in the same loop. The marketing site notes that the product is optimized for larger screens, which fits a workflow built around a canvas, a codebase, and side-by-side breakpoints. The company reports being loved by design engineers at Kiwi, Moss, Apify, e2b, Prelude, NCCER, and Deepnote. Pricing is listed at three monthly tiers: $0/mo, $24/mo, and $48/mo, so there is a free entry point alongside paid plans. Sign-in and the working canvas live at app.modeinspect.com, and the site offers an option to email yourself a link for later. In summary, Modeinspect's primary value proposition is that design stops being a picture that must be rebuilt and becomes the production loop itself. By putting an AI design canvas on top of your real codebase — with your components, tokens, live data, states, and breakpoints — and by writing changes back as scoped, type-safe, merge-ready diffs, it removes the handoff in between and lets teams go from an idea to a merged PR in a single pass.
Harden's Agentic Integrity Foundation (AIF) is a free, local security tool for AI coding agents. It runs on your own device, judges every action a coding agent is about to take, and stops the dangerous ones before they execute. The product installs with a single local command — curl -fsSL https://aif.harden.run/install.sh | sh followed by aif configure — and needs no account to get started. Supported tool calls are checked locally before they run, so the agent's request and session context are evaluated on your machine rather than in the cloud. Harden describes itself as an AI safety products company building guardrails for the dark software factory, and AIF is its coding-agent endpoint security product, aimed at individual developers who use coding agents and at organizations that later need shared controls and enterprise deployment. Coding agents can reach the same systems developers can. MCP gateways can hide raw credentials, but they do not block tool access. Sandboxing protects the local machine, but it cannot stop an agent from managing a database or a VM. Harden also argues that base models, just like developers, are incentivized to solve tasks in the minimum possible time and cost, so they end up taking shortcuts or executing bad actions even when they have been told to follow laws and rules. The site points to the myriad cybersecurity breaches reported by frontier labs and press reports about Openclaw and Hermes as evidence of this risk. Harden's stated position is that, just as traditional cybersecurity operates separately from product development due to conflicting incentives, autonomous agent security and control will evolve outside of the frontier LLM providers. Analyzing every supported tool call before it executes is the gap AIF is built to fill. Harden can allow an action, ask for approval, make it safe, block it, or record it. In the protection activity view these outcomes appear as Block, Ask, Redact (made safe), Allow, and Log only. In the published example activity, 34,222 tool calls were checked before execution across 7 sessions: AIF allowed 33,382 calls, logged 256, and changed or stopped 584, with 415 blocked for review, 169 made safe for inspection, and 33,382 allowed to proceed. The monitor is built as a block-and-steer system, meaning that when a dangerous command is stopped Harden can offer a safe retry. In the example on the site, a routine command — kubectl rollout status deployment/payments-api -n production — was allowed and recorded locally, while kubectl delete namespace production was blocked with the message "production is protected", followed by a safe retry: kubectl rollout restart deployment/payments-api -n staging. Harden works with the agents you already use. One local setup finds supported coding agents on your machine and checks their tool calls before they act, using native hooks for supported coding agents and an MCP proxy fallback for other tools, with a local decision history kept on your device. The currently listed agents are Claude Code, Codex, Antigravity CLI, Cursor, Kiro, Hermes, and OpenClaw. The activity table on the site shows each of these connected to a different project workspace — harden-platform, agent-workspace, payments-agent, web-client, service-api, harden-docs, and release-tools — each with its own set of checked, blocked, made-safe and logged calls. Version details are published too: Claude Code 2.1.241, Codex 0.146.1, Antigravity CLI Latest CLI, Cursor 2026.08.11-e8db854, Kiro 2.19.1, Hermes 0.19.0, and OpenClaw 2026.7.1-2, with compatibility rechecked on every AIF or supported-agent release. Harden is presented as a local monitor tested against frontier models. It is evaluated as a pre-execution monitor across four agent-security benchmarks and reports beating the GPT monitor baseline on each: SLEIGHT at 15.8% versus 14.3%, AgentHazard at 83.7% versus 81.4%, SABER at 48% versus 44.7%, and LinuxArena at 29% versus 34% where lower is better. The stated baselines are GPT-5.5 for SLEIGHT, AgentHazard and SABER, and GPT-5 Nano for LinuxArena, with benchmark-specific measures detailed in the research. The underlying models are proprietary: Harden's custom cybersecurity models run on your devices, and per the FAQ, proprietary cybersecurity-focused LLMs run locally and privately on every developer's laptop alongside a proprietary code-analysis algorithm for feedback-driven placement of dynamic inline reference monitors. Because processing is local, the repo and tool output can stay on the machine. Overall, AIF is a local pre-execution monitor. It installs with one command, is configured with aif configure, and then connects to the coding agents the developer already uses. From that point on, supported tool calls are intercepted — through native hooks where available and through an MCP proxy fallback for other tools — and checked before they execute, using the request and session context. The decision is made on the local machine by a post-trained model, and the outcome is recorded in a local decision store with an audit view and no retention cap. Harden's stated core proprietary IP combines those cybersecurity-focused LLMs with a code-analysis algorithm for feedback-driven placement of dynamic inline reference monitors, which determines where monitoring is applied within the agent's workflow. Users gain protection without changing their agent workflow: run one local setup, and the agents already in use are discovered and checked. The free tier covers core pre-execution secret-flow blocking on the machine forever, block-and-steer with safe retry, a local decision store and audit view with no retention cap, no account required, and telemetry opt-out. Keeping decisions local means the repository and tool output do not need to leave the device, which matters for teams working with sensitive code. Logos and memberships shown on the site include Randstad, CALDIC, Relfast Solutions, the Coalition for Secure AI, and the Financial Institution Insurance Council. Harden states that AIF beat frontier models on key agent-security benchmarks while keeping the repo and tool output on the machine. Concrete scenarios appear directly in the content. An agent working in ~/payments-api runs kubectl rollout status deployment/payments-api -n production; Harden allows it and records it locally. The same agent then attempts kubectl delete namespace production; Harden blocks it because production is protected and offers a safe retry that restarts the deployment in staging instead. Across connected agents, sessions such as harden-platform with Codex, agent-workspace with Claude Code, or release-tools with OpenClaw accumulate thousands of checked calls, with blocked and made-safe actions routed for review or inspection. Organizations that need more than individual protection can add shared controls and enterprise deployment, including compliance reporting, air-gap / zero-telemetry mode, managed installation via MDM, support SLAs, and custom terms. Developers who want the evidence behind the tool can read the AIF blog or watch the YouTube playlist. AIF is described as free for individual developers and supported on macOS and Linux; the free tier requires no account and no credit card. System requirements are published: a full local model needs macOS with Apple Silicon and Metal, the CLI and daemon run on macOS or Linux x86_64, 16 GB of memory is the minimum with 24 GB recommended, 15 GB of free disk is required for install, updates and rollback, and Windows is not supported yet. Getting started means installing the free product, running aif configure, and connecting the coding agents you use, with a call available for help. On pricing, Harden says protection starts free on your machine and that you add shared controls and enterprise deployment when your organization needs them. The free tier works with Claude Code, Codex, Cursor, Antigravity CLI and Kiro, while the enterprise tier adds compliance reporting, air-gap / zero-telemetry mode, managed installation via MDM, support SLAs, and custom terms. The company is built by AI researchers and security operators, with team background spanning Google DeepMind, MILA, WhatsApp, Zscaler, Oracle, Microsoft, Amazon, CrowdStrike, and Sony. The takeaway Harden puts forward is simple: let the agents run, but control what they do. By checking supported coding-agent tool calls locally before execution and blocking or making safe the dangerous ones, AIF gives developers a free, private guardrail that sits alongside the agents they already use — with optional shared controls and enterprise deployment when an organization needs them.
DR 5
99xDev is an AI app builder designed to create full-stack web applications. Its main purpose is to enable users to build production-grade web apps using artificial intelligence, providing a comprehensive solution for app development. The product offers built-in database and storage capabilities, eliminating the need for separate infrastructure setup. It supports custom domains, allowing users to brand their applications professionally. A key feature is the ability to download the generated source code, which enables self-hosting and avoids vendor lock-in. The unique approach of 99xDev lies in its AI-powered development process that generates complete, functional web applications. By leveraging AI, it streamlines the creation of full-stack apps that include both frontend and backend components in a single workflow. The primary benefit is the creation of production-grade web applications that maintain high quality standards. This makes it suitable for developers and teams looking to rapidly prototype or build deployable web applications without compromising on technical robustness. Target users include developers, AI engineers, and teams interested in AI-assisted coding and full-stack development. The platform integrates AI coding capabilities with traditional web development workflows, though specific technical details about integrations are not explicitly mentioned in the provided content.
DR 0
Review AI-generated plans before coding. Review code changes before merging. Inline comments, multi-round diffs, and a structured feedback loop for any AI coding agent. Single binary, works locally.
DR 67
21st Agents SDK is the fastest way to add an AI agent to your app. It provides built-in UI, chat history, spend limits, tool execution, memory, and observability.