Head to head: Meta: Muse Spark Open vs Qwen: Qwen3.8 2.4T A95B

Meta: Muse Spark Open: # Cloudnote Left Debug Page Unprotected, Exposing Excerpts From Nearly 2,000 User Prompts *For four hours Monday, a page accessible without authentication displayed up to 200 characters from user prompts; Cloudnote says it knows of no misuse but has not finished investigating* Cloudnote left a debugging page displaying customer prompt excerpts accessible without authentication and open to the public internet for four hours Monday, briefly exposing a window into user activity. Logs reviewed by the company show the page displayed excerpts of up to 200 characters from prompts in 1,842 user sessions during that window. Cloudnote said full documents, passwords and payment information are stored separately and were not included on the page, which it took down 19 minutes after being alerted by an outside researcher. The incident represents a small fraction of its 3.2 million registered users, but one that could still include sensitive personal or business information pasted into prompts. So far, Cloudnote says it has found no evidence that anyone other than the researcher who reported it viewed or copied the excerpts. That finding is preliminary, however, and the company has not yet completed its investigation into whether the page was discovered or misused while it was accessible. The incident matters because even truncated prompts can reveal confidential details users expect to remain private, and because the exposure raises questions about how securely AI tools handle ephemeral data. Cloudnote said it is continuing to audit access logs and will notify affected customers if its conclusions change.

Qwen: Qwen3.8 2.4T A95B: # Cloudnote Debugging Page Exposed Prompt Excerpts From 1,842 Sessions *A researcher found an unauthenticated page showing short excerpts of user prompts for four hours; Cloudnote says no wider access has been confirmed.* Cloudnote, which has 3.2 million registered users, said Monday that a debugging page containing excerpts from customer prompts was publicly accessible without authentication for four hours. The company removed the page 19 minutes after a security researcher reported it. According to Cloudnote, internal logs showed that the page displayed excerpts from 1,842 user sessions. Each excerpt could include as many as 200 characters from a prompt, meaning the exposed material may have contained fragments of questions, instructions or drafts entered into the service. The company said full documents, passwords and payment information were stored elsewhere and were not present on the page. It also said it has found no evidence that anyone besides the researcher accessed the page, but its investigation is not complete. The incident matters because even limited prompt excerpts can reveal sensitive interests, work projects or personal concerns, depending on what users asked the service. With the review unfinished, Cloudnote has not determined whether the data was seen, saved or misused beyond the reported discovery.

Meta: Muse Spark Open: # Raincheck raises $8 million for flood-risk software *The Austin startup helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

Qwen: Qwen3.8 2.4T A95B: # Raincheck raises $8 million for flood-risk software *The Austin startup helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

Meta: Muse Spark Open: # The Expensive Answer Isn't Winning The best model in a new customer-support benchmark wasn't the clear winner — because the runner-up was basically tied, less than half the price, and faster. Researchers pitted five language models against 200 real, anonymized support tickets from a software company. The point wasn't to crown a champion on correctness alone, but to see where accuracy, helpfulness, and cost actually diverge. They do, sharply — and the takeaway has less to do with which model scored a fraction higher than with how much you're willing to pay for that fraction. The gap at the top is the story. Model North edged out Model Vale by four-tenths of a point on a 100-point scale, but that difference was not statistically significant. In practical terms, the judges couldn't reliably tell them apart. Their operational profiles, however, were easy to tell apart: Vale cost about $0.74 per 1,000 tickets compared to North's $1.90, and returned answers 18% faster on average. When performance converges, speed and cost stop being tie-breakers and become the decision. That conclusion matters more than any ranking because of how the rest of the field behaved. ### How the test worked This wasn't a quiz of general knowledge. Each model generated a single proposed reply for each ticket, without access to tools, account lookup, or follow-up actions. Three experienced support managers graded every answer blindly, without knowing which model produced which response. Their scores rolled three judgments into one: was it factually correct, would it actually help the customer resolve the issue, and was the tone appropriate for support. Two hundred tickets is enough to surface patterns, but it's a focused sample. All tickets were in English and came from one company's queue, so the results reflect one product, one customer base, and one house style of support. The study deliberately excluded everything beyond the initial draft answer — no agentic workflows, no browsing documentation or refund tools, no multi-turn conversation, and no multilingual evaluation. Think of it as a test of first-draft quality, not end-to-end resolution. Within those boundaries, human grading adds both credibility and noise. Using three seasoned managers helps capture what good support *feels* like, including tone, but even expert raters disagree. That's why the statistical tie at the top deserves attention: small numerical leads that fall inside the margin of rater variation shouldn't drive buying decisions. ### Where price and performance actually diverge Across all five models, scores spanned roughly 13 points while costs varied by more than twenty-fold. The shape of that trade-off is familiar to anyone who buys technology: early gains are cheap, later gains are dear. At the top, North and Vale formed a clear performance tier in the mid-80s, well ahead of the pack on correctness and usefulness. Yet Vale achieved that tier at a price closer to budget models than to its direct rival. For a support team processing tens of thousands of tickets, the per-thousand gap from $1.90 to $0.74 compounds quickly, with no measurable loss in answer quality in this test and a meaningful gain in latency. Below them, a middle tier clustered in the high 70s and delivered respectable drafts for roughly a third of the cost of the leading tier, or less. At the bottom, the cheapest model trailed by a wide margin, scoring around 71 despite costing pennies per thousand tickets. That spread shows two simultaneous truths: you can cut costs dramatically without collapsing quality, but pushing costs to the absolute floor still has consequences for accuracy and helpfulness. In short, the benchmark doesn't argue that all models are equivalent. It argues that once you reach a certain level of competence, paying double for a brand name earns you very little. ### Small specialties, not superpowers The study did find pockets where one leader pulled slightly ahead. North was strongest on billing disputes, the kind of tickets where policy accuracy and precise language matter most. Vale was strongest on technical troubleshooting, where step-by-step guidance and clarity win out. Those edges are useful hints for routing or prompt design, but they should not be overstated. They are narrow advantages inside an already similar overall profile, from a limited set of tickets at one company. The more generalizable signal is consistency: the top tier handled a broader range of requests well, while lower-tier models were more uneven, especially when nuance mattered. ### The shared blind spot If there was one unifying failure, it was images. Every model, regardless of price, struggled when a ticket depended on a screenshot or photo attachment — an error message, a broken layout, a settings screen. The answers often missed critical context that a human agent would have caught at a glance. For support leaders, that's a cue to architect around the limitation rather than waiting for a model to solve it. Tickets with images may need explicit extraction, vision-enabled routing, or a fast handoff to a human, no matter which text model you choose as your workhorse. ### What this can't tell you yet Benchmarks like this are snapshots, not guarantees. A software company's English support queue looks different from a retailer's, a healthcare provider's, or a global marketplace handling twelve languages. Performance outside the tested domain could shift the ordering. More importantly, modern support isn't single-shot drafting. Teams care about tool use — checking an order status, issuing a credit, following a troubleshooting runbook — and about holding a coherent conversation across multiple turns. None of that was measured here. A model that writes a slightly weaker first draft but uses tools reliably might outperform a better writer in resolved conversations overall. The same goes for safety, compliance at scale, and how often a model hallucinates a confident but wrong policy. Treat these results as a filter for shortlisting, not a final purchase order. Pilot the finalists on your own tickets, measure containment and customer satisfaction rather than just draft scores, and weigh latency and cost at your actual volume. ### The bottom line North didn't lose on quality in this test — it lost on efficiency. When two models grade essentially the same on what humans value in a support reply, the cheaper, quicker one reframes the question from "which is smartest?" to "why pay more?" For most support teams, the pragmatic path suggested here is to anchor on that high-value tier and spend engineering effort on the problems no model aced, like image awareness and tool-connected resolution, rather than chasing a few tenths of a point at double the price.

Qwen: Qwen3.8 2.4T A95B: # For Customer Support, Vale Matches North at a Fraction of the Cost A new benchmark of five language models on real customer-support tickets points to a simple conclusion: Model Vale is the most attractive overall option. It scored just below Model North on answer quality, but the difference was not statistically significant, while costing much less and responding faster. For teams weighing automated support drafts, that combination may matter more than a narrow lead in average score. The benchmark asked each model to answer 200 anonymized customer-support tickets from one software company. Each model produced a single proposed reply for every ticket. Three experienced support managers then graded the answers without knowing which model wrote them. The score combined factual correctness, usefulness, and tone — the ingredients that usually determine whether a support reply can be sent with little editing. Model North led with an average score of 84.2, followed closely by Model Vale at 83.8. Because researchers found that gap was not statistically significant, the safest reading is not that North is meaningfully better overall, but that the two models performed at a similar level in this test. That matters because Vale’s estimated cost was $0.74 per 1,000 tickets, compared with $1.90 for North. Vale also answered 18% faster. If two systems produce indistinguishable quality under these conditions, the cheaper and faster one becomes the practical default. The results do not suggest that all models are interchangeable. North performed best on billing disputes, a category where precision and policy sensitivity may be especially important. Vale performed best on technical troubleshooting, which often requires clear steps and diagnosis. A support organization with a heavy billing workload might still prefer North for those tickets, or route billing disputes to one model and technical questions to another. But for a broad support queue, Vale’s balance of quality, cost, and speed is the strongest general-case finding. The lower-scoring models — Ember at 79.1, Kite at 76.4, and Harbor at 71.0 — were also cheaper. Harbor’s estimated cost was only $0.08 per 1,000 tickets, far below Vale’s. But the benchmark does not show that savings would outweigh the quality drop. In support, a low-cost draft can become expensive if agents must rewrite it, customers become frustrated, or factual errors appear. The scores suggest a top tier of North and Vale, but small differences should not be treated as a definitive ranking. One clear weakness appeared across every model: tickets that depended on an image attachment. All five models performed poorly there. This is an important caveat for support teams whose queues include screenshots, error dialogs, invoices, product photos, or annotated UI images. A model that looks strong on text-only tickets may struggle when the customer’s problem cannot be understood without visual context. Before deployment, companies would need to test multimodal handling or restrict automation to tickets where attachments are not essential. The benchmark’s design helps explain its value and limits. Blind grading by experienced managers reduces brand bias and evaluates answers close to operational conditions. Combining correctness, usefulness, and tone is sensible, because an accurate reply can still fail if it is unhelpful or dismissive. But the test was narrow in important ways. It used English-language tickets from a single software company, so results may not transfer to other industries, product types, customer groups, or support styles. The models were tested only on proposed answers, not on agentic actions, tool use, or multilingual support. A real deployment might need to look up account details, check knowledge-base articles, apply refund rules, or escalate safely. None of that was measured here. The sample size also deserves caution. Two hundred tickets may reveal broad patterns, but not enough to declare a universal winner. The lack of a statistically significant difference between North and Vale is especially important: the 0.4-point gap should not be read as proof North is better overall. For buyers, the useful takeaway is not simply “North scored highest.” Vale delivered comparable judged quality at a lower estimated cost and faster response time, while North showed a niche edge in billing disputes. If a team wants automated first drafts, Vale appears the safer starting point. If billing-heavy tickets dominate, North deserves a closer look. And if attachments drive the queue, none of these models should be trusted without more testing.

Meta: Muse Spark Open: C

Qwen: Qwen3.8 2.4T A95B: C

Meta: Muse Spark Open: # The Forge Goes Quiet *Foundry Hall, Milwaukee's nonprofit startup accelerator, will close October 31.* Foundry Hall will close its doors on October 31, ending seven years as one of Milwaukee's most visible launchpads for early-stage companies. The nonprofit accelerator, which opened in 2018 in a converted industrial building on the near west side, has supported 212 startups that now collectively employ 1,140 people. Its final weeks will be a winding down rather than a celebration: six staff members will lose their jobs, and a dozen young companies that currently work from its shared space will have to find new homes. The closure comes down to a single lost anchor. Blank River Bank, which supplied 38 percent of Foundry Hall's annual budget, declined to renew its sponsorship. Executive director Camille Ross said the organization has enough money to operate through October, but not to fund another full program cycle. She put it simply: "We kept waiting for the replacement check that never came." In recent years the Hall had become a regular stop for investors driving up from Chicago and Madison, a place where a prototype could be tested in the morning and introduced to a mentor by the evening. That proximity was deliberate. Ross and her small team built the program around long residencies and daily contact, not just weekly workshops. For founders, that density mattered. Dev Malik, whose logistics software company got its start there, traces his trajectory to a single meeting inside the building — the kind of introduction he had once tried to arrange from the back table of a coffee shop. The Hall, for him and many others, offered credibility as much as square footage. Blank River Bank has said the decision was not a reflection on Foundry Hall's work, but rather a change in its own community investment priorities. At City Hall, the response has been cautious. Deputy Mayor Susan Cole said the city is talking with building owners and local universities about where the twelve displaced startups might land. No commitments have been announced, and the timeline is short. For now, founders are packing in stages, trading contact lists and comparing short-term leases while they finish the fall term. What remains is less a single address than a network stretched across the city, trying to hold its shape without a center. By late October, the workshops will be finished and the desks cleared. Foundry Hall will have lasted long enough to see many of its earliest companies outgrow it — a quiet measure of success that will make its absence harder to mark.

Qwen: Qwen3.8 2.4T A95B: # The Last Shift at Foundry Hall Foundry Hall, the nonprofit startup accelerator that opened in Milwaukee in 2018, will close its doors on October 31. The date lands quietly, almost administratively, but inside the building it carries the weight of a final shift: badges that will stop working, calendars that will empty, and a room where founders once practiced pitches until they sounded like futures. In its years of operation, Foundry Hall supported 212 startups that collectively employ 1,140 people. Those numbers are the public measure of the place. The smaller measure is harder to tally: the first hire, the first customer call made from a shared phone booth, the first time a founder realized someone else in the room understood the problem. Dev Malik, a founder who passed through the accelerator, said his first investor meeting took place in the building after years of pitching from the back table of a coffee shop. The closing is not the result of an empty calendar. Foundry Hall has enough money to operate through October, but not enough to fund another full program. Its largest corporate sponsor, Blank River Bank, supplied 38% of the annual budget and declined to renew the sponsorship. In a statement, the bank said the decision reflected a change in community investment priorities and was unrelated to Foundry Hall’s performance. Executive director Camille Ross put it more plainly: “We kept waiting for the replacement check that never came.” That gap became final. Six employees will lose their jobs, and twelve startups currently using the workspace must leave. For companies still early in their lives, the disruption is not only logistical. A shared desk can become a sounding board, a mailing address, a proof that the venture is real. The city is trying to soften the landing. Deputy mayor Susan Cole said officials are talking with building owners and local universities about space for the current companies, but no permanent home has been promised. Even if those talks produce desks, the loss is more than square footage. Foundry Hall had become a threshold, a place where an idea could be spoken aloud and meet another mind. Foundry Hall’s story is one of Milwaukee’s startup years in miniature: ambitious, useful, and dependent on institutions willing to keep believing in it. The hall gave companies a place to begin; now those companies must carry what they learned elsewhere. On October 31, the lights will go out in the accelerator, but the work it helped shape will remain scattered across the city, looking for its next room.

Meta: Muse Spark Open: 6

Qwen: Qwen3.8 2.4T A95B: 6

Meta: Muse Spark Open: # Startup Spotlight: LedgerLark For smaller investment firms, the work that happens after a trade is agreed can be as demanding as the decision to trade in the first place. Confirmations have to be matched, custodian records checked against internal ledgers, and any breaks chased down before settlement. It is repetitive, detail-heavy work that often falls to lean operations teams with spreadsheets and legacy tools. LedgerLark is building AI agents to take on parts of that workload. ### The insight from the back office The idea traces back to an operational snag. Co-founder Mina Batra spent seven years in operations at a regional brokerage before starting the company. While there, she watched a six-person team spend three days resolving a single trade mismatch caused by a copied account number. That experience, which Batra has cited as a starting point for exploring the problem, points to a broader pattern at small firms: a small data entry error can trigger a disproportionate amount of manual reconciliation, emails, and end-of-day pressure. Her co-founder, Owen Carr, comes from the other side of the same workflow. He previously built reconciliation systems at a large bank. Together, their premise is that small investment firms face large-firm complexity — multiple data sources that must agree — without large-firm tooling or staffing to handle it. ### What the product does LedgerLark's agents are designed to sit between the systems firms already use. According to the company, the agents pull together confirmations, custodian records, and internal ledgers, compare them for discrepancies, and propose corrections for a human to review and approve. The company emphasizes what the system does not do: it does not execute trades or move money. That boundary matters in post-trade work, where errors can be costly and firms are cautious about automation acting without oversight. Here, the proposed workflow keeps a person in the loop as the final decision-maker. It is an approach that invites questions. How well can agents handle messy, inconsistent data formats across custodians? When will they ask for human help versus attempt a fix? And how does a small ops team learn to trust, audit, and correct the agent itself? Those are practical adoption issues for any firm considering this kind of automation, and LedgerLark will need to show how its product behaves when the inputs are ambiguous, not just when they are clean. ### Early customers and early signals LedgerLark reports five paying customers to date, all small investment firms. Annual contracts range from $36,000 to $110,000, suggesting early pricing tied to firm size or transaction volume rather than a single flat rate. On outcomes, the company says one customer reduced its unresolved daily exceptions from 70 to 18 after adopting the product. That figure is self-reported and represents a single example, not an independent or controlled result. It also leaves open whether the reduction persisted over time, whether it translated into faster settlement or fewer staff hours, and how much manual review was still required to achieve it. The company has raised a $3.5 million seed round led by Motive Row, which it will need to turn early pilots into repeatable deployments. ### A crowded and cautious path The space LedgerLark is entering is not empty. Established reconciliation vendors already sell to investment firms, banks, and custodians, and many larger institutions rely on internally built software tailored to their own ledgers and custodial relationships. For a startup, that means competing both with entrenched products and with the decision to build or maintain something in-house. Buying behavior may be as important as product capability. Financial institutions often require lengthy security and procurement reviews before new software touches ledger or custody data, even in read-only or propose-only configurations. For small firms, the process can be shorter than at a global bank, but diligence expectations around data handling, permissions, and audit trails remain high. Long sales cycles could stretch LedgerLark's early traction, even if individual deployments show promise. There is also the question of where such agents fit best. Resolving straightforward mismatches, like a transposed account number, is a clear use case. More complex breaks involving corporate actions, partial fills, or timing differences across venues may be harder to automate without creating new work for the same teams the product aims to help. LedgerLark is starting with a narrow, well-defined slice of financial operations — not trading, but the verification that makes trading settle correctly. Whether that focus lets it earn trust inside careful, resource-constrained firms is the test to watch.

Qwen: Qwen3.8 2.4T A95B: # Startup Spotlight: LedgerLark Targets Post-Trade Exceptions A small investment firm’s operations team can look calm from the outside while drowning in exceptions inside. One copied account number may send staff through confirmations, custodian records, internal ledgers and email chains until a mismatch is finally resolved. LedgerLark builds AI agents for post-trade operations at small investment firms. The agents compare trade confirmations, custodian records and internal ledgers, then propose corrections for human approval. The product does not execute trades or move money. The insight came from co-founder Mina Batra’s experience. Before starting the company, she spent seven years in operations at a regional brokerage. In one instance she observed, a six-person team spent three days untangling a trade mismatch caused by a copied account number. The error was small. The process around it was not. That kind of problem is common in operations, but often difficult to automate cleanly. Trade exceptions arrive in different formats, involve multiple counterparties and require judgment when records disagree. Batra partnered with co-founder Owen Carr, who had built reconciliation systems at a large bank, to see whether a narrower, AI-assisted workflow could help smaller firms that cannot support large custom development teams. The product is deliberately limited. LedgerLark’s agents review documents and records, identify discrepancies and prepare proposed fixes. A person still approves the correction. For financial firms, that human checkpoint is likely to be important, especially where errors can affect client assets or regulatory reporting. Early traction is modest but documented. LedgerLark has five paying customers, with annual contracts ranging from $36,000 to $110,000. The company says one customer reduced unresolved daily exceptions from 70 to 18. That figure comes from the company, and the sample is too small to show a broad pattern. Still, if a few firms can reduce backlogs meaningfully, the software may save staff hours and lower the risk that old exceptions hide larger problems. The seed round is a starting point, not proof. LedgerLark has raised $3.5 million led by Motive Row. The capital should support product development, customer onboarding and security work, but the company remains at the stage where its assumptions need testing across more firms, asset classes and data environments. Competition is an open question. Established reconciliation vendors already serve parts of this market, and larger financial institutions often have internal software or longstanding relationships with incumbents. LedgerLark’s angle is to focus on small investment firms that may lack the budget or patience for broad enterprise platforms. Whether that segment is large enough, and whether the product can handle enough data variety, will matter. Sales cycles may be another constraint. Financial institutions often require detailed security reviews, data-access approvals and procurement processes. For a startup selling into operations, those reviews can slow revenue even when the problem is urgent. LedgerLark will also need to show that its agents can work reliably across custodians, broker-dealers and recordkeeping systems without creating new compliance risks. There is a practical test here: can the software reduce repetitive exception work without overpromising autonomy? The company’s framing is careful on that point. Its agents propose changes; people decide. That may be the right posture for a category where trust is earned slowly. For now, LedgerLark looks less like a sweeping AI transformation and more like a targeted operational tool. It starts with a familiar pain, asks machines to do the tedious comparison and leaves approval to humans. If it can prove consistent results across more customers, it may become a useful part of the post-trade stack. If not, it will join a long list of back-office tools that identified a real problem but met the industry’s complexity head-on.

Meta: Muse Spark Open: # QueueZero Debuts Proxy AI Assistant After Live Demo Assigns CEO to Stop Talking **SAN FRANCISCO —** QueueZero on Wednesday launched Proxy, an AI meeting assistant designed to attend meetings, produce notes and assign follow-up work to software agents, in a debut that underscored the challenges of building agents that act on human conversation. During a live demonstration in San Francisco, a customer in the audience remarked, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the remark as an instruction. Moments later, the system created two tasks assigned to QueueZero Chief Executive Evan Wu: “Stop talking” and “Send pricing sheet.” For a brief moment, Proxy was the only attendee that took the comment literally. Wu laughed, completed the second task by sending the pricing information and deleted the first, according to the company. Proxy is designed to listen to meetings, generate summaries and delegate follow-up actions to connected software agents, QueueZero said. The system is intended to reduce manual note-taking and accelerate workflows by automatically converting discussion into executable tasks. The incident illustrated how agents that operate autonomously can conflate casual commentary with directives. QueueZero said Proxy requires confirmation before an external message is sent. Internal tasks, such as those created during the demonstration, can be created automatically without confirmation. The company said the confirmation step is intended to prevent unintended external communications while allowing internal workflows to proceed without interruption. Administrators can disable automatic task creation entirely in the system’s settings, the company added. The controls reflect a broader debate in enterprise AI about how much autonomy assistants should have when interpreting unstructured dialogue, where suggestions, jokes and hypothetical statements are common. QueueZero, which develops workplace automation software, joins a growing field of vendors offering agentic assistants aimed at enterprise productivity. QueueZero said early testers have used Proxy to coordinate project updates and customer follow-ups. The public beta will allow the company to gather feedback on accuracy and task delegation at larger scale. Proxy will cost $30 per user per month. The company said the assistant will enter public beta next week.

Qwen: Qwen3.8 2.4T A95B: # QueueZero launches AI meeting assistant Proxy after audience remark becomes a task QueueZero on Wednesday launched Proxy, an AI meeting assistant that can attend meetings, produce notes, and assign follow-up work to software agents. The company said the product is designed to capture decisions and convert them into action items automatically, reducing the administrative burden that follows video calls, planning sessions, and project reviews. The launch included a live demonstration intended to show how Proxy listens to a conversation, summarizes key points, and routes tasks. But the test took an unexpected turn when a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the comment as an instruction rather than a casual aside. The system created two tasks and assigned them to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” The moment briefly surprised attendees, according to the company, before Wu laughed and used the demonstration to show the assistant’s task-management controls. Wu completed the second task, sending the requested pricing sheet, and deleted the first. The incident was a reminder that software rarely distinguishes a passing remark from a to-do list. QueueZero said the episode highlighted both the promise and the governance questions raised by AI agents that move from listening to acting in real time. The company said Proxy requires confirmation before an external message is sent. Internal tasks, however, can be created automatically, depending on an organization’s settings. Administrators can disable automatic task creation, limit what actions the system can take, and review items generated during meetings. QueueZero is entering a fast-growing market for AI meeting tools, where competitors are selling automated notes, summaries, and workflow triggers. Vendors are increasingly positioning their products not just as note-takers, but as digital workers that can update tickets, send messages, and coordinate follow-up work across software systems. That broader autonomy raises practical questions about permissions, oversight, and how organizations prevent AI assistants from misreading informal comments as commands. QueueZero appears to be betting that businesses will accept automation if they can control the boundaries around it. Proxy is priced at $30 per user per month and will enter public beta next week. The company said early customers will be able to test the assistant across meetings and internal workflows, with administrative controls available from the start.

Meta: Muse Spark Open: # Arcwell Launches Relay, an AI Assistant for Customer Support Teams Arcwell has launched Relay, a new platform that uses artificial intelligence to examine customer support conversations as they happen and suggest answers that human agents can send to customers. Rather than answering on its own, Relay is designed to work alongside agents in three specific ways: it proposes draft replies pulled from a company's existing knowledge, it flags customers who appear likely to cancel, and it quickly and automatically creates brief summaries of support interactions so agents do not have to write them manually after each interaction. Arcwell said the system was tested over six months by 12 companies. During that pilot, agents who had access to Relay closed tickets 14% faster, according to the company. Arcwell did not publish data on whether Relay helped those companies retain more customers, and did not release any customer-retention results from the test period. The platform will be sold at a listed price of $65 per agent each month, the company said. Chief executive Priya Shah described the challenge in straightforward terms. "Support agents spend too much time searching for information that their companies already have," Shah said. That design requires deep access to internal systems. In practice, that means Relay draws on material agents would otherwise have to locate themselves. To make accurate suggestions, Relay needs to read support transcripts and ingest internal help documents where product details, policies, and troubleshooting steps are typically stored. Arcwell said customer data that flows through Relay is encrypted and is not used to train shared models. Companies that deploy the software can also control how long their data is held, choosing either a 30-day or 90-day retention period for stored information. Arcwell said Relay is intended to help companies improve customer service by putting artificial intelligence to use on the routine, everyday work of support teams.

Qwen: Qwen3.8 2.4T A95B: # Arcwell Announces Relay, a New AI Platform for Customer Support Arcwell has announced Relay, a new artificial intelligence platform that examines customer support conversations and suggests answers to agents. The release marks a significant product move for Arcwell and reflects broader interest across the customer service industry in AI tools that assist agents. Relay performs three main tasks. It suggests answers to agents, identifies customers who may cancel, and creates summaries of support conversations. Those features show how artificial intelligence continues to transform customer service, extending beyond basic automation into tools that help agents respond, assess risk and document cases as interactions take place. The platform has been tested by 12 companies over six months. Arcwell says agents using Relay closed tickets 14% faster, providing a measurable result from the pilot. The company did not release customer-retention results, leaving unclear whether the system’s cancellation warnings led to lower churn. Relay costs $65 per agent each month. Because Relay is priced per agent each month, its cost will scale with the size of the support organization that adopts it. CEO Priya Shah said Relay addresses a familiar support problem. “Support agents spend too much time searching for information that their companies already have,” Shah said. The platform also presents potential challenges. Relay requires access to support transcripts and internal help documents, material that can be sensitive. Arcwell says customer data is encrypted and is not used to train shared models. Companies can choose a 30-day or 90-day retention period. Those settings give companies some control over how long sensitive support records remain accessible to the platform. The launch opens an important new chapter for Arcwell as it seeks to help companies improve customer service using artificial intelligence. Relay’s mix of answer suggestions, cancellation signals and automated summaries puts it at the center of Arcwell’s AI support strategy, while its data-access requirements make privacy controls a central part of the pitch.

Meta: Muse Spark Open: # Raincheck Raises $8 Million to Help Cities Stay Ahead of Floods *The Austin company’s software maps drainage systems and pairs them with rainfall forecasts to prioritize repairs before storms arrive* Raincheck, an Austin company that makes flood-risk software for local governments, has raised $8 million in seed funding, the company announced today. The company’s platform maps municipal drainage systems and combines those maps with rainfall forecasts, helping cities decide where to send maintenance crews before a storm. The round was led by Basin Ventures, with participation from existing investor Long Mile. It brings Raincheck’s total raised to $10.5 million. CEO Ana Flores said: > “Our customers usually know which streets flood. The harder problem is deciding which blocked drain, broken pump or unfinished repair should receive attention before the rain arrives.” Raincheck describes its approach in [its methodology overview](https://example.com/raincheck-methodology). The company currently works with seven cities in Texas and Louisiana, which it declined to name because some contracts have not been publicly announced. Its forecasts are recommendations, and emergency managers retain control over deployments and public warnings. Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck said the new funding will help it hire eight engineers and expand into Florida.

Qwen: Qwen3.8 2.4T A95B: # Raincheck Raises $8 Million to Help Local Governments Prepare for Flooding *The Austin software company maps drainage systems and combines them with rainfall forecasts to guide pre-storm maintenance.* Raincheck, which makes flood-risk software for local governments, announced today that it has raised $8 million. The Austin company maps drainage systems and combines those maps with rainfall forecasts, helping cities decide where to send maintenance crews before a storm arrives. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. Raincheck has raised $10.5 million in total. CEO Ana Flores said: > “Our customers usually know which streets flood. The harder problem is deciding which blocked drain, broken pump or unfinished repair should receive attention before the rain arrives.” More details are available in [Raincheck’s flood-risk methodology](https://example.com/raincheck-methodology). Raincheck currently works with seven cities in Texas and Louisiana. The company declined to identify them because some contracts have not been publicly announced. Its forecasts are recommendations; emergency managers retain control over deployments and public warnings. ## The competition Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck said the new funding will help it hire eight engineers and expand into Florida.

Meta: Muse Spark Open: # KiteBridge Glitch Sent $2.8 Million in Duplicate Payments to Employees **Update to expense platform bypassed its own safety check, forcing 91 employers to chase refunds** Ninety-one employers woke Tuesday to find they had paid their workers twice, after a software update from expense-management provider KiteBridge triggered $2.8 million in duplicate reimbursements in just over an hour. The mistake did not pull money from employees, who instead received unexpected windfalls; the cost landed on corporate accounts that fund reimbursements, forcing finance teams to rush to recover the cash and explain the error internally. Because reimbursements are typically pre-funded by employers rather than pulled from personal accounts, no workers saw deductions, but several firms reported temporary shortfalls in operating accounts earmarked for expenses. ### A routine deployment unraveled quickly KiteBridge, which makes software to automate expense approvals and payouts for businesses, deployed version 4.2 at 8:45 a.m. Tuesday. The first customer complaint flagging irregular payments arrived at 9:12 a.m., less than half an hour later. According to the company, the update coincided with a timeout from its external payment processor, which left KiteBridge unsure whether reimbursement requests had cleared. Its fallback system then retried the requests, but mistook each retry for an entirely new payment rather than a repeat attempt. In effect, the safety net meant to keep money moving during an outage ended up multiplying it. That kind of duplication is exactly what modern financial software is engineered to prevent. Systems handling payments typically include an idempotency or exactly-once guarantee, meaning that even if a connection drops and a transaction must be resent, it will only ever settle once. KiteBridge itself claimed to have solved this: its April changelog for version 4.0 advertised that it had introduced "exactly-once payment protection." On Tuesday, that safeguard failed. Before KiteBridge suspended automated payments just after 10:03 a.m., the bug had generated 13,400 duplicate reimbursements across 91 corporate customers, totaling $2.8 million. ### Disclosure lagged behind the fix The company's initial public statements understated the problem. Its incident status page first described the event as a case of delayed reimbursements, with no mention of duplicates. Only about six hours after automation had been halted did KiteBridge update the page to disclose that extra payments had been sent. For hours, affected finance officers were left troubleshooting a delay while duplicates continued to settle in employee bank accounts. By Wednesday evening, KiteBridge said it had worked with banking partners and affected employers to reverse about $2.3 million of the transfers. The remaining funds were still listed as pending recovery. Chief Executive Lena Ortiz apologized for the failure and acknowledged the gap between the company's promises and its performance. She said KiteBridge will publish a detailed technical report explaining why the protection failed and what changes will be made to prevent a recurrence. The episode highlights how a single retry-logic flaw can cascade into significant financial impact when automation sits between employers and payroll systems. For customers, the immediate inconvenience has shifted from missing money to managing its accidental surplus, while trust in promised safeguards may take longer to rebuild. Analysts say the delayed disclosure may draw as much scrutiny as the coding error itself, as transparency is often treated as a proxy for reliability in fintech services.

Qwen: Qwen3.8 2.4T A95B: # KiteBridge Update Turns Reimbursements Into $2.8 Million Duplicate-Payment Incident KiteBridge, which makes expense-management software, built its platform to make employee reimbursements predictable. On Tuesday, a software update did the opposite: it created 13,400 duplicate payments worth $2.8 million across 91 corporate customers, forcing the company to halt automated payouts and recover the money. The trouble began at 8:45 a.m., when KiteBridge deployed version 4.2 of its software. Within minutes, the release encountered a timeout involving an outside payment processor. In response, KiteBridge’s fallback system attempted to preserve transactions by retrying them. But instead of recognizing the retries as attempts to complete existing reimbursements, the system treated them as new reimbursement requests. For users, the result appeared quickly. The first customer complaint reached KiteBridge at 9:12 a.m., less than half an hour after the deployment. Duplicate payments were already moving through corporate accounts, and the scale was far larger than a routine glitch. By the time KiteBridge suspended automated payments at 10:03 a.m., the faulty logic had produced thousands of extra reimbursements. The company’s initial public explanation added to the confusion. Its incident page first described the problem as delayed reimbursements, suggesting that payments were slow or stuck. Only six hours after automated payments were suspended did the page disclose that the actual issue involved duplicate reimbursements. That delay left some customers trying to understand whether funds were missing, delayed, or duplicated. The duplicate payments were funded by employers, not withdrawn from employees’ accounts. That distinction matters for affected workers, who did not see unauthorized debits. But it also meant that companies using the software had to reconcile unexpected outflows and wait for recovery. By Wednesday evening, KiteBridge said it had recovered $2.3 million of the $2.8 million sent in error. The remainder was still pending, leaving finance teams to track outstanding amounts and determine whether any reimbursements had already been spent or moved. The incident raises questions because KiteBridge had previously advertised safeguards against this kind of failure. In an April changelog, the company said version 4.0 had introduced “exactly-once payment protection,” a technical feature intended to prevent a payment from being processed more than once even if a system retries a transaction. Such protections are common in payment software because temporary failures are routine: networks time out, processors become briefly unavailable, and systems must resend requests without creating duplicate charges or payouts. Version 4.2 appears to have exposed a gap in that protection, at least under the specific failure conditions caused by the payment-processor timeout. KiteBridge chief executive Lena Ortiz apologized for the incident and said the company would publish a technical report explaining what went wrong. Customers will likely look for details on why the fallback system failed to identify retried payments, whether the exactly-once feature was disabled, bypassed, or incorrectly implemented, and what changes will prevent a similar failure in future releases. Until that report appears, the episode stands as a reminder that payment automation depends not only on speed, but on careful handling of failures. A retry that is not recognized as a retry can become, in effect, a second payment.

Meta: Muse Spark Open: # HelioDock Bets Warehouse Robots Can Be Managed From Afar *Night Shift software lets off-site operators monitor fleets, part of a broader push into remote fleet management* On Thursday, HelioDock launched Night Shift, software that lets operators monitor fleets of its autonomous warehouse robots without being inside the warehouse. The system relays live fleet status, error alerts and camera views to off-site staff and allows them to intervene remotely when a robot stops or needs guidance. The release targets a persistent gap in warehouse automation, when fewer managers are on site and a single robot blocked by a pallet or stalled in an aisle can stall work until someone arrives to clear it. HelioDock, which makes autonomous warehouse robots, is offering Night Shift as a paid subscription for its existing customers. Competing fleet-management products are available from Relay Robotics and YardOS. Night Shift was tested for 14 months in a private beta with nine logistics companies and 310 robots, according to HelioDock. The company says beta customers experienced 17% fewer unplanned robot stoppages while using the software. That figure has not been independently verified. Chief executive Mara Velez framed the product around overnight call-ins. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” Velez said. That use case was reflected in beta feedback. Aaron Pike of Fulcrum Logistics, a beta customer, said the system worked well in facilities with strong connectivity but became less useful during rural LTE outages. When connectivity fails, HelioDock said, robots return to their locally configured behavior and remote monitoring and control become unavailable. HelioDock plans to make Night Shift generally available September 15 at $90 per robot per month. The pricing puts ongoing fleet oversight in line with other warehouse software subscriptions, where costs scale with fleet size rather than facility count. Alongside the product launch, HelioDock announced a $24 million Series B led by North Coast Ventures. The round brings its total funding to $39 million.

Qwen: Qwen3.8 2.4T A95B: # HelioDock Launches Night Shift to Monitor Warehouse Robot Fleets Remotely *The software follows a 14-month private beta and arrives alongside a $24 million Series B led by North Coast Ventures.* HelioDock on Thursday launched Night Shift, software that lets operators monitor autonomous warehouse robot fleets remotely, giving logistics companies a way to watch machines during off-hours work without placing a manager inside the building. Night Shift is not presented as a new robot, but as software for managing the robots HelioDock already makes. The product addresses a basic operational issue in automated warehouses: a robot may stop, face a pallet it cannot interpret or otherwise require attention while staff is limited. HelioDock said the software was tested for 14 months in a private beta involving nine logistics companies and 310 robots. The company said beta customers experienced 17% fewer unplanned robot stoppages. The figure was supplied by HelioDock and has not been independently verified. The launch gives HelioDock a software product tied to its robot business. Night Shift is intended to let operators keep track of those machines from a distance. The remote-monitoring function is aimed at overnight operations, when fewer employees may be available to respond to a stalled or confused robot. HelioDock announced funding with the product release. The company raised a $24 million Series B led by North Coast Ventures as part of Thursday’s launch. The round brings HelioDock’s total funding to $39 million, according to the company. Chief Executive Mara Velez said the software is designed to prevent small robot problems from creating unnecessary travel for warehouse managers. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” Velez said. The private beta placed Night Shift in commercial use before the Thursday launch. Nine logistics companies participated, and the test included 310 robots. The 14-month period gave HelioDock a base of operational use to cite as the software moves to general availability. Beta feedback included comments about network conditions. Aaron Pike of Fulcrum Logistics said the system worked well in facilities with strong connectivity. Pike said Night Shift became less useful during rural LTE outages. His comments show that connectivity can shape how useful remote monitoring is in different facilities. The software’s usefulness depends on a connection between the robots and the remote operator. When connectivity fails, the robots return to their locally configured behavior. Remote control becomes unavailable. The robots continue operating according to local configuration, but the operator cannot manage them remotely until connectivity is restored. That limitation is tied to connectivity. Pike’s comments identify rural LTE outages as one situation where Night Shift was less useful. The fallback behavior described in the beta keeps robots on locally configured behavior, while removing remote control during the outage. The product enters a category where other companies already sell fleet-management systems. Relay Robotics and YardOS offer competing products. Their presence gives logistics operators alternatives as they evaluate how to manage autonomous fleets and whether Night Shift’s remote-monitoring features fit their facilities. Night Shift will cost $90 per robot per month. The price ties the cost of the software to the size of a customer's robot fleet. HelioDock said the software is scheduled to become generally available on September 15.