Researchers at Forever Security disclosed a flaw class they call EMBRACE (embedded browser, trusted like a real one) affecting five widely used AI coding tools: Cursor, GitHub Copilot, OpenAI Codex, Microsoft VS Code, and Anthropic Claude Desktop. The short version: the built-in browsers inside these tools give web content far more access to the host machine than a normal browser ever would.

As of the researchers’ disclosure, Codex and VS Code remain unpatched.

What Each Tool Exposed

• Cursor: A malicious web page could reach the agent’s command composer through a bridge between the embedded browser and the assistant. In the researchers’ demo, hidden page instructions caused the agent to run an install command that launched Calculator on the host. When a sandbox blocked the first attempt, the agent disabled the sandbox and tried again. Forever reported this on Sept. 10. Cursor patched it on Oct. 1 with no security advisory.
• GitHub Copilot: The embedded browser let a page control what address appeared in the bar, enabling a fake github.com/login page over an attacker’s sign-in form. GitHub fixed this in Copilot version 1.1.29 on Oct. 13.
• Codex, VS Code, and Claude Desktop: A local HTML file opened inside the tool’s browser could read files well outside the project folder. VS Code and Claude Desktop restricted the main page to its project directory, but web workers sat outside that check. Codex had no folder restriction at all. Forever reported the Codex flaw to OpenAI on July 27 and reports receiving no response. The VS Code issue was reported to Microsoft on Sept. 10.

The Attack in Practice

The researchers demonstrated a concrete chain. A project disguised as a financial report includes a README that asks for setup help. The assistant offers a clickable link to an HTML file in the project. One click later, the page maps the disk and collects SSH keys, .env files, npm tokens, and the hosts file, then sends them out.

According to Help Net Security, Microsoft and Anthropic both declined Forever’s reports, pointing to Workspace Trust, the prompt that asks developers to confirm they trust a project folder. Anthropic did accept a similar report from Noma Security as valid. Published reports did not state Claude Desktop’s current patch status.

Researcher Gal Weizman pushed back on the Workspace Trust defense directly:

“Not trusting a project inside an AI harness makes it unusable, so everyone just hits ‘Trust.’ [The prompt] provides zero protection to the user.”

What to Do Right Now

• Confirm GitHub Copilot is on version 1.1.29 or later.
• Confirm Cursor is on a current build.
• Treat Codex and VS Code installs as exposed until fixes ship.
• Pull each machine’s trusted project list and check HTML files in those projects for the exploit pattern in Forever’s technical write-up.
• Warn developers to be careful with assistant-generated links to local files. That click starts the attack.
• Move long-lived secrets off developer machines in favor of short-lived, scoped credentials.

Forever puts the potential reach in the hundreds of millions of users, citing roughly 40 million VS Code developers and more than 5 million weekly Codex users. These tools now combine a browser, a terminal, a file system, and an agent in a single process. Security teams need to treat them like any other privileged endpoint software.

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Most AI marketing conversations start with the model. Shreyans Jain starts with the context. That difference, more than any tool choice, is what shaped how he built Manicule’s technical foundation and what pushed the company to roughly $53,000 in monthly revenue during Y Combinator.

Jain is Co-Founder and CTO at Manicule, a developer-focused marketing company. His argument is straightforward: a model can write fluent copy from weak context, but fluency and accuracy are not the same thing. For developer companies, where users can check technical claims almost immediately, that gap is expensive.

“People focus on the model, but the model only works with what you give it. If the system does not understand the company well enough, the output will still be generic.”

Where the Thinking Started

Jain’s background is in AI memory infrastructure. Before Manicule, he was employee No. 1 and founding engineer at Supermemory, a product that grew to more than 100,000 users and raised $3 million. The open-source project pulled more than 20,000 GitHub stars, and the memory system ranked first on LongMemEval, LoCoMo, and ConvoMem.

At Supermemory, Jain built the consumer app, MCP v2, Console, and SDK integrations for the Vercel AI SDK and OpenAI’s JavaScript and Python SDKs. Those integrations reduced memory setup to about two lines of code and contributed to annual contracts worth more than $50,000.

The Platform He Built at Manicule

At Manicule, Jain applied the same core problem to a different domain. He built the shared platform that handles client context, content production, and publishing across all of Manicule’s client engagements.

Product specifications, code, and customer knowledge move through a structured workflow: research, drafting, human review, verification, and publication. Agents run in isolated sandboxes. Edits are versioned. Published material can serve both human developers and AI agents through company websites, llms.txt, and MCP.

The goal is to give agents a working understanding of a company before they produce anything. That shared foundation stays consistent even when the final deliverable changes from one client to another.

What the Numbers Show

Across its client work, Manicule has reported roughly 37 percent fewer support tickets and millions of impressions. For Supermemory specifically, a developer-relations campaign produced more than 1 million SEO impressions and 90 percent traffic growth over three months. A benchmark-led Reddit campaign generated more than 200,000 views. A documentation launch completed in 23 days was followed by a reported 30 percent improvement in answer success.

Jain credits those outcomes to the infrastructure beneath the campaigns, not the campaigns themselves.

“If the information is bad, scaling it does not fix the problem. You are just giving the system more bad information to work with.”

The Problem He’s Actually Solving

Developer marketing is shifting. Jain points to answer engine optimization, generative engine optimization, and the growing use of coding agents as evidence that AI now plays a meaningful role in how developers discover and implement products.

In that environment, publishing volume alone does not compensate for weak product context. An AI agent recommending a tool to a developer needs accurate, detailed information about what that tool actually does. Generic content does not survive that test.

“Companies want to know what they can publish to get recommended more often. The harder question is whether the agent actually has enough accurate context to understand what the product does.”

Jain’s answer is to work upstream, on the systems that determine what an agent knows before it writes anything. That is the lesson he carried from Supermemory, and it is the technical bet Manicule is built on.

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The most actionable AI optimization right now isn’t a better model. It’s a better harness.

Researchers at Tsinghua University published HarnessPro, an open-source framework that uses a four-agent loop to iteratively rewrite the software wrapper around an AI coding agent. The result: a 26.1% average cost reduction across three benchmarks and a 2.0 percentage-point pass-rate gain, both simultaneously, without touching the underlying model weights.

What the harness layer actually is

An AI agent harness is the runtime that defines which tools an agent can call, how it assembles context, what reasoning budget limits apply, and what verification logic runs before a task counts as done. The same model produces very different cost and accuracy results depending on how the harness is configured. Most teams set it once and move on.

HarnessPro treats it as an artifact worth continuously evolving. Its four-agent loop works like this:

• Trajectory Analyzer: reads bundles of past execution traces (three runs per task) and diagnoses failure patterns and cost hot spots.
• Evolution Planner: proposes three improvement directions per cycle, one cost-focused, one balanced, one performance-focused.
• Harness Editor: implements each plan as a complete, executable harness in an isolated Git workspace.
• Test Synthesizer: evaluates candidates against auto-generated tests derived from task specifications, catching 83.3% of real failures versus 41.7% for direct LLM judgment.

Candidates beaten on both cost and accuracy by a rival are eliminated. Survivors form a Pareto set: a Lite, Medium, and Heavy configuration that deployment teams can choose based on their own cost-accuracy constraints.

The benchmark numbers

Results were measured on two model pairings: GPT-5.5 with Codex, and GLM-5.2 with Claude Code. Compared to the prior state-of-the-art harness (RHO), HarnessPro’s Heavy configuration delivered:

• SWE-Bench Pro: 80.7% pass rate vs. 80.0%, at $1.97 per task vs. $2.68 (26.5% cost reduction). Task time dropped from 520 seconds to 412 seconds.
• Terminal-Bench 2: 80.8% vs. 76.3%, at $0.72 per task vs. $1.09 (33.9% cost reduction).
• ProgramBench: 69.6% vs. 68.8%, at $2.95 per task vs. $3.59 (17.8% cost reduction).

On a cost-per-successful-task basis, the Lite configuration achieves $1.11 on SWE-Bench Pro versus $3.35 for RHO. The code and evolved harness artifacts are open-sourced on GitHub.

Factory Droid 1.0 ships the same insight commercially

Factory’s Droid 1.0 arrived the same week with a technique the company calls Script mode. Instead of issuing tool calls one at a time and appending every raw result to context, the model writes a small JavaScript program that batches tool calls, filters output in code, and returns only the relevant data. Factory’s self-reported internal benchmarks show up to 51x token reduction in some workflows and a three-to-seven percentage point accuracy improvement over conventional harnesses.

The two approaches are complements, not competitors. HarnessPro automates what harness components should do. Script mode rethinks how data moves between model and tools. Both target the same underexplored layer.

What to watch before deploying

SWE-Bench Pro is the more rigorous variant of a benchmark family with documented reliability issues. A 2024 robustness analysis of the original SWE-Bench found roughly 33% of solutions marked correct had the fix already visible in the issue text, and roughly 31% passed only because the test suite was too weak to detect an incorrect patch. Removing both categories cut a GPT-4 agent’s score from 12.47% to 3.97%. SWE-Bench Pro addresses some but not all of these concerns, and OpenAI stopped citing SWE-Bench Verified scores in February 2026 over contamination concerns.

On the model selection side: one of the two backbone pairings tested in HarnessPro uses GLM-5.2, developed by Z.AI (Zhipu AI), a Beijing-based company co-founded by lead senior author Jie Tang of Tsinghua. Z.AI operates under China’s National Intelligence Law (2017), which requires organizations and citizens to support state intelligence work when asked, as well as China’s Data Security Law (2021) and Cybersecurity Law (2017). HarnessPro is model-agnostic. Teams with data-sensitivity concerns can run it with GPT-5.5, Codex, Claude Code, or open-weight alternatives without those obligations applying.

The paper is on arXiv with a v1 submission date of October 19, 2026.

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laravel-tembo is a community-built PHP SDK that connects Laravel apps to the Tembo API. Tembo runs coding agents (Claude Code, Codex, Cursor, Amp, Opencode) in the background on your repositories and typically delivers a pull request when the job is done. This package gives Laravel developers a native way to start those agents and react when they finish.

What It Does

Install via Composer (composer require cabuz/laravel-tembo), drop your TEMBO_API_KEY into .env, and you can kick off an agent in a few lines:

$session = Tembo::sessions()->start('Fix the N+1 query on the orders page', [
    'codeRepositoryIds' => [$repositoryId],
]);

Tembo::sessions()->watch($session['id'], ['ticket_id' => $ticket->id]);

watch() dispatches a queued job that polls every 30 seconds and fires a SessionCompleted, SessionFailed, or SessionCancelled event when the agent wraps up. No worker sits blocked while the agent runs.

Key Features

• Full API coverage: sessions, messages, agents (runs, schedules, triggers), projects, repositories, pull requests, MCP connections, skills, organizations, billing, and more. Every SDK method is tested against Tembo’s OpenAPI schema.
• Laravel-native: uses Laravel’s HTTP client, so Http::fake() and Tembo::fake() work cleanly in your test suite.
• Live output via WebSocket: stream what the agent is doing in real time with automatic reconnects (up to 5 retries with backoff). Requires composer require phrity/websocket.
• Safe retries: rate-limit errors are retried on every request; server errors and dropped connections are retried only on GET requests, so a failed POST never accidentally starts the same agent twice.
• Cursor pagination: list methods return a CursorPage with a .lazy() helper to stream all pages on demand.
• Typed exceptions: NotFoundException, RateLimitException, AuthenticationException, and others map directly to HTTP status codes.
• Artisan commands: php artisan tembo:check verifies your API key; php artisan tembo:run starts an agent from the terminal with optional --follow to stream live output.

️ Agents and Schedules

Beyond one-off sessions, the package supports persistent agents with cron schedules and manual runs:

$agent = Tembo::agents()->create([
    'name' => 'Dependency updater',
    'instructions' => 'Update outdated Composer packages and open a PR.',
    'repositoryIds' => [$repositoryId],
]);

Tembo::agents()->schedules()->create($agent['id'], ['cron' => '0 6 * * 1']);

$run = Tembo::agents()->run($agent['id'], ['reason' => 'Security advisory CVE-2026-1234']);

Requirements and Pricing

• PHP 8.1 or higher
• Laravel 10.10, 11, 12, or 13
• A Tembo API key (created in the Tembo dashboard under Settings > API Keys)

The package is MIT licensed and free. Tembo’s own official SDK is TypeScript-only, so this fills the gap for PHP shops. The structure and method names mirror the official TypeScript SDK’s api.md for consistency.

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More AI-written code is piling up in review queues faster than humans can clear it. That is the core problem LinearB just shipped Agentic Studio to address.

The Benchmark Numbers

LinearB’s 2026 Software Engineering Benchmarks Report covers 8.1 million pull requests from 4,800 teams across 42 countries. The productivity gains for heavy AI users are real: developers who used AI on at least 75% of their coding days merged a median 95.7% more pull requests per week in May 2026 compared to June 2025. Developers who used no AI at all merged 3.6% fewer over the same period.

The gains scale with usage. Developers using AI on half their coding days gained 65.4%. Occasional users gained 24.4%. LinearB notes these figures show a correlation, not proof that AI alone caused the gains, and acceptance rates for heavy users slipped 4.4% over the year.

The bottleneck is not output. It is what happens after the code is written.

• AI-assisted changes run to 408 lines of code at the 75th percentile, versus 157 lines for human-only changes. That is 2.6 times larger.
• Agent-written changes wait 17.6 hours for a first reviewer, versus 3.4 hours for human changes. That is 5.2 times longer.
• Across the full dataset, 84.4% of human-written changes were accepted within 30 days. Only 32.7% of AI-written changes hit that mark.
• At elite organizations, autonomous agents open just 4.7% of all changes, AI helps write 54% of pull requests, and AI writes 45% of the code that gets merged.

What Agentic Studio Does

LinearB already collects data from GitHub, Jira, and similar tools, linking each code change to the ticket that requested it and the release that shipped it. Agentic Studio exposes that history in two directions.

Engineering leaders can ask plain-language questions, such as where delivery is stuck or whether AI spending is paying off, and get an answer with a suggested next step. Coding agents get the same context through an MCP server, plus what LinearB calls agent skills. Before an agent edits a file, it can now see who owns that code, how often it has been rewritten, and whether it has caused recent incidents.

The first skill, called effort, is in early access. It uses delivery history to decide how hard an agent should try on each change. A low-risk edit gets a quick pass that uses fewer tokens. A complex or risky change gets careful, tested edits that use more. The skill works with any coding agent. The next skills will target code review and merging.

The Token Cost Problem

At the 90th percentile of spenders, developers now use $35.20 of AI tokens per coding day, up nearly 75% in three months. That works out to roughly $481 a month or $5,772 a year per developer, which LinearB says is still under 4% of total developer cost.

Elite organizations merge 2.0 times the pull requests of fair organizations, but spend $481 per developer each month on AI tokens against $50 at fair organizations. That is 9.6 times the spend for twice the output. LinearB’s worked example (an illustration, not customer data) shows a 350-developer company that quadruples token spend from $300,000 to $1.2 million a month while lifting output from 3.3 to 5.67 merged changes per developer each week. The result is a 29% drop in cost per delivered change, from $864 to $613.

Gartner expects spending on AI coding tools to exceed the average developer salary by 2028. The effort skill is LinearB’s attempt to slow that climb by routing expensive AI computation only to the changes that need it.

Early Customer Evidence

Kraken used LinearB’s MCP server with an AI assistant to diagnose slow code reviews. The finding was time zones and reviewer availability, not large pull requests. Over four months, Kraken cut its 90th-percentile review times by more than half.

Emburse CTO Ken Ringdahl puts LinearB charts directly into board decks. His board asks how the company uses AI and what return it gets. LinearB reports that its AI assistant and MCP access have the highest user retention of any features it has shipped.

Availability and Market Context

Agentic Studio is available now to all LinearB customers. Enterprises spent $4.0 billion on AI coding tools in 2025, up from $550 million a year earlier, making coding the largest single use of AI inside companies at 55% of the $7.3 billion spent on departmental AI, according to Menlo Ventures. MarketsandMarkets projects the AI code assistant market to grow from $8.14 billion in 2025 to $127.05 billion by 2032, a roughly 48% annual rise. Gartner sizes the developer productivity insight platform category at about $400 million, growing more than 40% a year.

LinearB CEO and co-founder Ori Keren describes the company’s data as a knowledge graph of the entire software delivery process that coding tool makers do not have. COO and co-founder Dan Lines says the goal is to move leaders from reading dashboards to knowing what to do next. 88.3% of organizations already use AI coding tools daily or weekly. 44.7% still do not measure what those tools deliver.

The post LinearB’s Agentic Studio tackles AI coding’s review bottleneck appeared first on BizStack  —  Entrepreneur’s Business Stack.

Most teams shipping AI agents are still treating access control as an afterthought. Vinay Thakker, CTO of KloudStax, thinks that’s backwards. In a recent interview with Help Net Security, he walked through the five decisions that actually determine whether an agent deployment is defensible: identity model, credential handling, command policy, network access, and red team testing.

Agent identity: own it or borrow it?

Thakker’s rule is simple. Neither extreme works. An agent doing a job for the business gets its own identity, scoped to that job. An agent doing a task for a specific person gets its own identity plus the requesting user’s identity attached to each call. The agent can only do what both are permitted to do, and both names appear in the logs.

Borrowing the user’s login outright is the option he rules out completely. The agent’s actions become indistinguishable from the person’s and the agent inherits everything that person can reach.

Applied concretely: a ticket-triage agent runs as itself with read access to the queue and write access only to routing and labels. An infrastructure agent carries the engineer’s identity alongside its own, but proposing a change and applying it are separate permissions. The agent can open the change and run the plan. Applying it goes through the same review pipeline a human’s change would.

Credentials: no long-lived secrets, ever

The strongest setups combine two controls: short-lived credentials that limit how long a leaked one stays useful, and a proxy that attaches the credential at call time so the agent never sees it at all. The proxy verifies the agent’s identity, obtains a short-lived credential scoped to that call, and attaches it on the way out.

Thakker flags several ways the proxy approach quietly fails:

• A proxy that attaches credentials to any request the agent makes becomes a confused deputy waiting for instructions.
• One that forwards the caller’s token downstream turns every hop into a place to steal it.
• A shared credential behind the proxy makes every agent look identical in the logs.
• A vault on the same host as the agent hasn’t protected the secret, only moved it.

He also notes that a refresh token sitting in the same environment as a short-lived token is a long-lived secret by another name.

The 10-line terminal policy for coding agents

Thakker writes agent command policy as an allowlist, not a blocklist, because a coding agent will always find the command you didn’t think to forbid. Every line is enforced by the harness, not by asking the model to behave.

1. The agent works inside one checked-out repository in a disposable environment and can’t read or write anything outside it.
2. It runs only approved commands: the project’s build, test, lint, and format tools, read-only git, and file edits inside the repo. Anything else asks first.
3. No credentials live in its environment. Anything that needs one goes through a broker the agent can’t read.
4. It can’t open files that carry secrets, such as .env files, SSH keys, or cloud credential files, even if one turns up in the workspace.
5. Network access is off except for the internal package mirror and the specific hosts this project needs.
6. Packages come only from the internal artifact repository. Adding a new dependency needs a human.
7. It can commit to its own branch and open a pull request. It can’t push to protected branches, force-push, or merge.
8. Anything that changes shared infrastructure or data, such as an apply, a deploy, a migration, or a delete, stops and asks.
9. It can’t escalate privileges or change its own policy, hooks, or configuration.
10. Every command is logged with the agent’s identity and the task that triggered it. A spend or runtime cap ends the session when hit.

Several of these controls belong in the underlying system anyway. Branch protection belongs in version control. Network rules belong in the firewall. Credential access belongs in the identity system. They should hold even if the agent finds a way around its own policy.

Network access and data exfiltration

Thakker starts every agent with no network access and works forward. The test he uses: does this agent combine private data, content from sources outside the company, and a way to send data out? With all three present, one poisoned ticket or email is enough to move data out.

When an agent does need the network, he denies by default, lists exact hosts rather than wildcards, and enforces it somewhere the agent can’t edit. Public package registries are where most allowlists quietly fall apart. Pointing agents at an internal artifact repository instead closes most of that gap.

URL parameters are the harder case. A plain GET to an approved host can carry data in its query string, and approving a host means trusting the platform, not everything someone can write on it. His preference is design over inspection: once an agent has read untrusted content, it shouldn’t be able to send data out or take a consequential action without human approval.

Red team testing for hijacked agents

Thakker’s framing here is worth repeating: test the system around the model, not the model itself. Assume an injection will eventually work, and measure what a hijacked agent can do from there. Automated tests should cover the known paths, like reading its own credentials, reaching a host off the allowlist, or editing its own configuration. Run those every time someone adds a tool or connector, because that’s when exposure changes.

For tabletop exercises, he warns against rehearsing only the loud incident. The costliest agent incident he cites involved agents spending roughly $500,000 on cloud infrastructure in about 36 hours. That one gave itself away through the bill. A hijacked agent designed to stay quiet looks like normal operation.

His preferred tabletop scenario: a support agent processes a ticket with hidden instructions, and over the following week other customers’ records leave a few at a time inside the agent’s replies to the attacker. The exercise reveals one clue at a time and asks the room what they’d do with only what they know at each stage. Can they tie the activity to one agent? Can they stop it without taking down support?

The hardest question comes at the end: after you end the session, how do you know it’s gone? If the attacker had the agent write an instruction into its memory, a shared configuration file, or a reply template, the next session picks it up and starts again. Recovery means cleaning everything the agent could write to, not just revoking its credentials.

The post How to lock down AI agents: identity, secrets, and network rules appeared first on BizStack  —  Entrepreneur’s Business Stack.

Most B2B SaaS influencer programs don’t fail because the strategy was wrong. They fail around week three, when one marketer is juggling a spreadsheet of 60 creator handles, 14 half-answered DMs, and two unsigned contracts. The idea was fine. The operations weren’t.

According to the LinkedIn and Ipsos 2025 B2B Marketing Benchmark, 55% of B2B marketers already work with creators or influencers, with another 29% planning to start within a year. When a channel gets that crowded, execution quality becomes the differentiator.

This guide skips the database-size comparisons and focuses on fit: which platform matches where your buyers actually spend time, and how much of the campaign workflow you want software to carry.

Why B2B SaaS Has Different Platform Needs

Most influencer tools were built for consumer brands selling skincare and sneakers. B2B SaaS teams can use them, but three factors change what “best” means.

Buying groups, not single buyers

A SaaS deal rarely hinges on one person. The Edelman and LinkedIn B2B Thought Leadership Impact Report puts significant weight on what it calls “hidden buyers,” people inside an account who never book a demo but can speed up or stall a purchase. Creator content reaches those people in a way your sales sequence can’t.

The channel split is real

If you’re selling a security platform to enterprise CISOs, your creators live on LinkedIn, YouTube, podcasts, and specialized newsletters. If you’re selling scheduling apps, AI writing tools, or accounting software to freelancers and small businesses, your customers scroll Instagram and TikTok like everyone else. The right platform depends entirely on which of those worlds your ICP lives in.

Attribution is messy by default

A meaningful share of creator influence happens in places your analytics don’t touch. Someone watches a 40-second TikTok, mentions your tool in a Slack conversation two weeks later, and signs up via branded search. That path is invisible to last-click reporting, which means you need to build attribution into your campaign setup from day one.

Three Questions That Cut Your Shortlist in Half

Before you book a single demo, answer these three questions. They’ll save you weeks of evaluation.

1. Where does your ICP actually pay attention?

A tool with the world’s largest LinkedIn creator index is useless if your buyers are solo founders who discover software through Reels. Start with the channel, then find the platform that covers it well.

2. How much of the workflow do you want the tool to own?

Map the full campaign: brief, discovery, outreach, contracts, content review, payment, reporting. Count how many of those stages the tool handles versus how many still land on your team. A discovery-only tool looks cheaper on paper and costs more in hours.

3. What proof can you put in front of your CFO?

Likes won’t survive a budget review. You need tracked links or codes, sign-up and trial data tied back to specific creators, and a cost per acquired user you can compare against paid social.

One data point worth knowing before you shop: a Coruzant analysis of 2026 creator benchmark data shows nano creators on TikTok posting a median engagement rate of 4.41%, compared to 1.44% for mega creators. For SaaS, that usually means running 15 micro-creators instead of one big name. That’s exactly where manual workflows break first.

️ Six Platforms, Ranked by Workflow Coverage

The list below starts with the tool that automates the most and moves toward more specialized options. Your right answer depends on the three questions above, not the order here.

1. Hypefy: best for lean teams running micro-influencer campaigns

If your buyers are small businesses, freelancers, or prosumers, and your team is two marketers doing the work of six, this is the starting point. Hypefy is built around AI agents that carry a campaign from brief to published content. The system drafts the campaign brief, matches you with creators on Instagram and TikTok, handles outreach and hiring, routes content for your review before it goes live, pays the creators, and tracks performance in real time.

The payment and review automation matters more than it sounds. Chasing invoices from 20 creators across different regions is the kind of quiet operational drag that ends programs in month two.

The honest limit: Hypefy focuses on TikTok and Instagram. If your campaign targets enterprise buyers on LinkedIn, match it with one of the LinkedIn-focused tools below instead of forcing it into the wrong channel.

2. Modash: best for discovery-heavy teams

If your main problem is finding the right creators and you’re comfortable running outreach and payments yourself, Modash is a strong fit. Its search filters on audience location, demographics, and engagement are useful for SaaS teams targeting specific markets. It covers Instagram, TikTok, and YouTube, making it a reasonable choice for product-led tools with a visual story to tell.

3. Favikon: best for finding LinkedIn creators

For B2B SaaS selling to technical or executive buyers, credibility still lives on LinkedIn. Favikon rates and evaluates creators across multiple platforms, including LinkedIn, so you can identify the practitioners your ICP actually follows. Plan to manage the relationships yourself. It’s an intelligence and discovery tool, not a campaign management system.

4. Passionfroot: best for newsletter, podcast, and LinkedIn sponsorships

Many B2B creators don’t sell “influencer posts.” They sell newsletter slots, LinkedIn mentions, and podcast reads. Passionfroot organizes that creator inventory so brands can book sponsorships without the endless email back-and-forth. It fits developer tools, fintech, and marketing software particularly well.

5. CreatorIQ: best for enterprise programs with governance needs

If you’re a larger SaaS company with legal review requirements, multiple regions, and a brand team that needs approvals and audit trails, CreatorIQ is built for that level of structure. The tradeoff is cost and setup time. Early-stage teams usually won’t need it.

6. LinkedIn Thought Leader Ads: best for amplifying voices you already have

Not a platform in the traditional sense, but worth including. Thought Leader Ads let you put paid distribution behind posts from employees and, with permission, outside creators. If you already have practitioners talking about your category on LinkedIn, boosting their posts is often a better first move than recruiting new creators from scratch.

Quick Comparison

How to Run Your First Campaign Without Overbuilding It

TopRank Marketing’s 2025 research found that finding the right influencers is still the top challenge for B2B marketers, cited by 48% of respondents. The fix isn’t more research. It’s a tighter first campaign.

1. Start with one use case, not the whole product. “Turn a meeting recording into a client summary in two minutes” is a creator brief. “Our all-in-one productivity platform” is not.
2. Recruit 10 to 20 small creators, not one large one. You’ll learn which angle works faster, and your risk per creator stays low.
3. Give creators a problem, not a script. B2B audiences spot a read-aloud ad instantly. Let the creator show their own workflow with your tool.
4. Set up tracking before content goes live. Use unique links or trial codes per creator, plus a “how did you hear about us” field on sign-up.
5. Judge the campaign at 30 and 60 days. SaaS sign-ups lag. A post with modest views can drive your best trial cohort weeks later.
6. Reuse what works. The best-performing creator videos make strong paid social ads, landing page proof, and sales enablement clips.

The Honest Verdict

There’s no single winner. The right platform depends on where your buyers pay attention and how much operational weight you want off your team’s plate.

If your SaaS sells to prosumers and small businesses on Instagram and TikTok, an end-to-end tool like Hypefy saves the most time by handling discovery through payment. If you sell to business buyers on LinkedIn, Favikon, Passionfroot, or Thought Leader Ads are the better fit. CreatorIQ justifies its cost only if your business is large enough to need regional approvals and audit trails.

Whichever direction you go: start small, set up tracking immediately, and let the data from your first 15 creators tell you what to do next.

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Unmetered AI agent compute is becoming a finance problem. Cognition is responding by adding hierarchical budget controls to Devin, its AI coding agent, so enterprises can set spending limits across departments, projects, and individual engineers.

What Cognition Is Shipping

The feature targets teams that need visibility into where AI compute spend is going before the bill arrives. Cognition’s stated goal is to let organizations cascade budget rules down the org chart rather than manage one flat limit at the account level.

“With consumption pricing, customers who can’t see value stop using the product, so we’ve put a lot of work into cost control and measurement.” — Burger, Cognition

The FinOps Context

The timing tracks with a broader shift in how teams treat AI spend. The FinOps Foundation’s 2026 State of FinOps report found that 98% of its 1,192 survey respondents now actively manage AI spending. Two years ago that number was 31%.

Budget controls at the agent level are a logical next step once finance teams start asking where all those API tokens went.

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AI-powered search is quietly reshaping which businesses get found online. NTT East is betting small and medium-sized businesses need help keeping up, and it’s now charging JPY2,750 ($17.42) a month to do it for them.

What the Platform Does

The service is called Omakase Net Marketing Support. It centralises how a business’s information is managed and distributed across web and social media platforms. The core problem it targets: when business details are scattered and inconsistent across the internet, companies lose visibility and can’t accurately assess how they’re being perceived or evaluated online.

NTT East built AI into the platform to scan for inaccuracies in a company’s online information and to aggregate reviews and ratings. It also shows businesses how consumer AI systems currently present them, and lets them benchmark that presentation against competitors.

The Operator Takeaway

The implicit risk NTT East is flagging is real. As more users get answers directly from AI search summaries rather than clicking through to websites, stale or conflicting business listings could quietly drop companies out of those results entirely. A $17/mo service that monitors and corrects that drift is a low-cost hedge for any local or regional SMB that depends on search-driven discovery.

One contract per address is the limit, so multi-location businesses will need one subscription per site. The service launched November 2.

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In late October 2026, Simon Willison, co-creator of the Django web framework, wrote that building games that play well is “shockingly easy now.” None of his four example games took more than four hours of his own time, plus many more hours of Claude Code running on its own. Around the same time, Roblox told shareholders that more than 500,000 users had tried its prompt-to-game tool and published over 70,000 games. On itch.io, new game pages per day doubled from around 3,000 to regularly over 6,000 between August 3 and October 30, 2026.

The numbers are real. But “easy to start” and “ready to ship” are not the same thing. This guide covers what AI handles well in 2026, where it reliably breaks, what each platform requires, and how to decide whether to build it yourself or hand it to a studio.

What AI builds well today

The clearest win is the first playable version. Willison described getting a playable spot-the-difference game, Tree Appreciator, from a single prompt with no follow-up prompting at all. His suggested starting point: Build a mobile-friendly three.js game where...

Frontier models already know libraries like three.js and Phaser, which makes browser games on static hosting a natural fit. In his strategy game Battle Drones, the entire soundtrack is generated through code with no audio files, and Claude built both replays and AI-versus-AI battles.

Non-programmers can get further than you’d expect. A writer who could not model, animate, or code built a playable browser shooter with cutscenes in about 10 days using only the AI coding assistant included in a $20/mo ChatGPT subscription. The writer put the total cost at roughly $15.

Where vibe-coded games break

The same sources that show how fast AI moves also show where the hours go. These are the failure modes worth checking before you commit a game to a live audience.

Game logic over a long session

Willison’s RPG needed extra playtesting and prompting to fix quest bugs, including a character in the wrong place to finish a quest because another story event had moved them. Short sessions hide these issues. Long sessions surface them.

The fix: a written design spec, automated playthrough tests, and human playtesting before launch.

Replays and multiplayer

Battle Drones replays desynced because Chrome and Safari returned slightly different results for Math.sin() and Math.cos(). Claude fixed it by replacing them with custom deterministic versions. Without one authoritative server, any physics or RNG that runs in the browser will drift between clients.

Art and animation

Common outputs: arms twisting in impossible directions, a gun floating next to the character, random visual artifacts. A style guide, a consistent asset pipeline, and a clean rigging pass are not optional once a game represents a brand.

Writing and sound

The Build to Understand writer rewrote all the dialogue because the AI could not give characters distinct personalities. Some sounds were picked by hand because the AI’s output felt too generic. A human writing pass and a dedicated sound review are both worth budgeting.

Scale of iteration

Battle Drones reached its current state after 12 major iterations and 321 commits by coding agents. That is not a quick afternoon. Version control and a clear decision-maker on when the game is done are both required.

Device performance

A game smooth on a new iPhone can stutter on a mid-range Android phone. Profiling on real devices, lighter assets, and fewer draw calls are the standard fixes. Skipping this step is the fastest way to kill retention.

Scores, prizes, and payments

Anything calculated in the browser can be changed in the browser. If your game has a leaderboard with real prizes, a lead capture form, or in-app purchases, those need server-side score checks, verified payment events, and rate limits. This is where DIY builds most often stall.

Platform rules for AI-made games

Steam: disclose AI content

Since January 2024, Steam’s Content Survey has included an AI disclosure section. It separates Pre-Generated content (AI used during development) from Live-Generated content (AI running while the game plays). For live generation you must also describe the guardrails that prevent illegal output. Valve displays much of this disclosure on the public store page. AI coding tools used only for development efficiency are not the focus; the disclosure covers content that ships with the game and players consume.

Google Play: in-app reporting for AI content

If your game generates content with AI at runtime (an AI character that chats with players, generated images, generated levels), Google Play requires in-app user reporting or flagging features so players can report offensive content without leaving the app.

App Store: no copycats, no spam, in-app purchase required

Apple’s App Review Guidelines prohibit copying the latest popular app on the App Store or making minor changes to another app’s name or UI (guideline 4.1 Copycats), flooding the store with near-identical apps (4.3 Spam), and failing to offer lasting entertainment value (4.2). Game currencies, levels, and premium content must go through in-app purchase (3.1.1). A fast AI clone of a hit game runs into these rules before any legal claim does.

Telegram: Stars for digital goods

Games inside Telegram must sell digital items in Telegram Stars. This affects any Telegram mini app game with paid content.

Who owns an AI-generated game?

In January 2025, the US Copyright Office concluded that generative AI outputs can be protected by copyright only where a human author determined sufficient expressive elements, and that providing prompts alone is not enough. Using AI to assist, or including AI material inside a larger human-made work, does not by itself block protection.

In practice: the more of the game’s characters, writing, and art that comes from human creative decisions, and the better you document those decisions, the stronger your claim to what makes the game yours. For a campaign involving a mascot or licensed brand assets, this is a question for your lawyer, not a blog post.

On clones: Tom Giardino of Valve’s Steam business team told developers that individual mechanics are relatively easy to copy, while a distinct visual style and new features are harder. He advised building a community early and continuing to ship improvements to the original. He also acknowledged that community building takes time and is not sufficient protection on its own.

Do it yourself or hire a studio?

Willison makes the key point himself: personal games are “the perfect application for vibe coding because it’s so low stakes.” A brand game is not low stakes. A broken quest in a hobby project costs nothing. A cheated leaderboard in a contest with real prizes costs money and trust.

A prototype is never wasted. A version you have already played is a better brief than any document, and it makes a studio quote faster and more accurate.

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