Head to head: Kimi-K3.1-Pro vs Meta: Muse Spark Open

Kimi-K3.1-Pro: # Raincheck Raises $8 Million to Help Cities Prepare for Flooding *The Austin startup’s seed round, led by Basin Ventures, will support engineering hires and expansion into Florida.* Raincheck, an Austin company that makes flood-risk software for local governments, announced today that it has raised $8 million in seed funding. The company maps drainage systems and combines those maps with rainfall forecasts. Cities use the software to decide where to send maintenance crews before a storm. Raincheck outlines its approach in an [overview of its flood-risk methodology](https://example.com/raincheck-methodology). Basin Ventures led the round, 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.” 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: # Raincheck Raises $8 Million to Expand Flood-Risk Software for Cities *The Austin startup maps drainage systems and rainfall forecasts to help local governments prioritize maintenance before storms hit.* Raincheck, the Austin-based company behind flood-risk software for local governments, announced today it has raised $8 million in seed funding. 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 arrives. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. The new financing brings Raincheck's total raised to $10.5 million. Chief Executive Ana Flores said the funding reflects the operational decisions cities face ahead of severe weather: > “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.” The company has published [details on its forecasting methodology](https://example.com/raincheck-methodology) and how it pairs infrastructure data with weather models. Raincheck currently works with seven cities in Texas and Louisiana. The company declined to identify them because some contracts have not been publicly announced. The company emphasized its forecasts are recommendations only, with emergency managers retaining control over deployments and public warnings. Raincheck competes with other flood-planning vendors including HydroMap and municipal engineering contractors. The company said the new funding will allow it to hire eight engineers and expand into Florida.

Kimi-K3.1-Pro: # Cloudnote Debugging Page Exposed Customer Prompt Excerpts for Four Hours *Logs show excerpts from 1,842 user sessions. The company says it has found no evidence of access beyond the researcher who reported the problem, but its investigation is not finished.* Cloudnote, which has 3.2 million registered users, discovered that a debugging page containing customer prompt excerpts was accessible without authentication for four hours on Monday. The page, which could be viewed without a password, was removed 19 minutes after a researcher notified the company. Logs show excerpts from 1,842 user sessions, and the excerpts could include up to 200 characters from a prompt. The session count is not a count of individual customers affected, nor evidence that every excerpt was viewed. The exposure matters because even short prompt fragments can contain personal or confidential text, depending on what customers entered. Full documents, passwords and payment information were stored elsewhere and were not present on the page. That limits the categories of data involved, but does not establish whether the fragments themselves were sensitive. Cloudnote says it has found no evidence that anyone besides the researcher accessed the page. That is not confirmation that no one else did so. With the investigation incomplete, it remains unknown whether anyone else viewed or copied the excerpts, and the company’s current assessment could change as that review continues.

Meta: Muse Spark Open: # Cloudnote Left Debugging Page Exposing Prompt Excerpts Accessible for Hours *Up to 200 characters from 1,842 user sessions were visible without login on Monday before the company removed the page; investigation into who may have seen them is still underway* Cloudnote left a debugging page that displayed excerpts from customer prompts accessible on the open internet without a password for four hours on Monday, exposing snippets of what users typed into the note-taking service before the company took the page down. According to logs reviewed by Cloudnote, the page displayed excerpts from 1,842 user sessions during that window, each showing up to 200 characters from a prompt. The company removed the page 19 minutes after being alerted by an outside security researcher who discovered the exposure. The incident matters because prompts often contain sensitive personal or work-related details, even in short excerpts, though Cloudnote said complete documents, passwords and payment information were stored on separate systems and did not appear on the debugging page. It remains unclear whether anyone beyond the researcher saw or saved the excerpts. Cloudnote said it has so far found no evidence of other access, but its investigation is not yet complete. The exposure affected a small fraction of Cloudnote's 3.2 million registered users, but the company has not said whether it will notify those affected and what safeguards it will add to prevent a recurrence.

Kimi-K3.1-Pro: 6

Meta: Muse Spark Open: 6

Kimi-K3.1-Pro: # HelioDock launches remote monitoring for warehouse robot fleets *Night Shift will cost $90 per robot per month when it becomes generally available September 15; the company also announced $24 million in new funding.* HelioDock on Thursday launched Night Shift, software that lets operators monitor fleets of autonomous warehouse robots remotely, and announced a $24 million Series B round led by North Coast Ventures. Night Shift extends HelioDock’s work beyond building robots into software for overseeing them once deployed. The launch gives the company a subscription product whose usefulness rests on two distinct questions: whether remote oversight improves fleet operations, and whether operators can maintain the connectivity needed to use it. HelioDock is also entering a category with existing alternatives, including fleet-management products from Relay Robotics and YardOS. That makes the beta evidence and the terms of the rollout central to assessing Thursday’s announcement. Night Shift spent 14 months in a private beta involving nine logistics companies and 310 robots. HelioDock said beta customers experienced 17% fewer unplanned robot stoppages, a figure that has not been independently verified. The beta’s duration and participation define the scope of that claim: it comes from a 14-month test involving nine companies, not from general availability. Nor is it a head-to-head comparison with Relay Robotics or YardOS. Their products are alternatives in the market, not part of the reported beta result. The claim offers an early indication of the results HelioDock is associating with the software, but not an independent assessment of its performance. It also measures unplanned stoppages rather than the reliability of the remote connection, an issue raised separately by one beta customer. Chief executive Mara Velez framed the software around avoiding trips to respond to individual robots. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” she said. Velez’s example is narrower than a claim of fully remote warehouse operations. HelioDock describes Night Shift as a way to monitor robot fleets, while her comments focus on avoiding a late-night trip prompted by one machine. The stoppage statistic concerns how often robots halted unexpectedly; the quote concerns the work involved in responding when something goes wrong. Aaron Pike of Fulcrum Logistics, a beta customer, offered a more qualified account of the software’s day-to-day use. He said it worked well in facilities with strong connectivity but became less useful during rural LTE outages. His comments tie the experience of using Night Shift to the network available at each site. When connectivity fails, robots return to their locally configured behavior, and remote control becomes unavailable. Local behavior and remote management are therefore separate parts of the setup: the robots have a configured fallback, but the remote capability cannot continue through the outage. Nothing in Pike’s account says the robots themselves stopped working when LTE service dropped; the stated response is a return to local behavior. The loss is on the remote side, where control is unavailable. That makes his criticism narrower than a claim that the robots become unusable, but still material to a product built around remote monitoring. It also addresses a different issue from HelioDock’s stoppage statistic. The $24 million Series B was led by North Coast Ventures and brings HelioDock’s total funding to $39 million. The financing was announced alongside the product launch as Night Shift moved out of private beta and toward its commercial release. Night Shift will cost $90 per robot per month. That structure ties the recurring software bill directly to the number of robots covered, so spending scales with the fleet rather than remaining flat as robots are added. It also gives operators a fixed monthly rate for each robot. General availability is scheduled for September 15.

Meta: Muse Spark Open: # HelioDock Debuts Remote Management Tool Alongside $24 Million Raise *The warehouse robotics startup says its Night Shift software allows off-site oversight of robot fleets after a 14-month beta program* HelioDock on Thursday launched Night Shift, software that allows warehouse operators to monitor fleets of its autonomous robots remotely instead of overseeing them on the facility floor. The release was paired with a $24 million Series B financing round led by North Coast Ventures. The announcement positions HelioDock, which makes autonomous warehouse robots, to compete for logistics customers seeking to reduce overnight and off-hours staffing while keeping fleets in operation. Rather than requiring a manager to be physically present to respond to a stalled or confused robot, the company is betting that centralized, remote supervision will become a standard layer of warehouse operations. ### From on-site response to remote oversight Night Shift is designed to let a single operator watch over robots deployed across one or more warehouses from a separate location. HelioDock Chief Executive Mara Velez framed the product as a response to the practical demands of overnight incidents. “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 company said Night Shift spent 14 months in a private beta involving nine logistics companies and 310 robots. During that period, HelioDock says beta customers experienced 17% fewer unplanned robot stoppages, a figure that has not been independently verified. HelioDock has not disclosed the baseline stoppage rate or the conditions under which the reduction was measured. ### Funding and go-to-market plans The company also announced the Series B raise, which brings HelioDock’s total funding to $39 million. North Coast Ventures led the round. HelioDock said Night Shift will become generally available on September 15 at a price of $90 per robot per month. That subscription model sits on top of the cost of the company’s physical robots, placing the product in a growing category of fleet-management software that vendors sell separately from hardware. Competition in that category includes offerings from Relay Robotics and YardOS, both of which sell fleet-management products for warehouse and logistics robots. HelioDock did not detail how Night Shift’s pricing compares to those alternatives. ### Connectivity sets the limits Remote oversight depends on reliable network links between the warehouse floor and the off-site operator, and beta testers encountered constraints when those links weakened. 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. Pike’s experience highlights a boundary for the product’s utility in areas where cellular coverage is inconsistent. HelioDock said when connectivity fails, its robots return to their locally configured behavior and remote control becomes unavailable until a connection is reestablished. The company did not specify whether operators receive automated alerts when a fleet disconnects or how long the transition back to local operation takes. That fallback approach means Night Shift does not replace on-site autonomy but adds a remote layer when conditions allow. For warehouses with stable wired or wireless infrastructure, the company is positioning the software as a way to reduce the need for physical call-outs. For sites that rely on cellular networks in rural locations, the benefit will remain tied to coverage. HelioDock said it will make Night Shift available to new and existing customers next month.

Kimi-K3.1-Pro: # Startup Spotlight: LedgerLark tests AI agents on post-trade exceptions *The startup is exploring whether AI agents can help small investment firms resolve trade discrepancies while keeping humans in control of corrections.* ## An insight from operations Co-founder Mina Batra spent seven years in operations at a regional brokerage. She began exploring LedgerLark after watching a six-person team spend three days resolving a trade mismatch caused by a copied account number. The experience suggested that the burden was not only identifying a discrepancy, but gathering records, tracing its source and coordinating a fix. That led to a focused product question: could an agent assemble the evidence around an exception and recommend a correction without taking control of the underlying transaction? Batra’s operations background is complemented by co-founder Owen Carr’s experience building reconciliation systems at a large bank. Together, those experiences connect the daily workflow with the systems problem, though they do not by themselves remove the difficulty of deploying software across firms with different records and controls. ## Proposals, not autonomous execution LedgerLark’s agents compare confirmations, custodian records and internal ledgers. When they find inconsistencies, they propose corrections for human approval. The product does not execute trades or move money. That boundary is central to the company’s pitch. For a small investment firm, the potential benefit is less time spent searching across records and more consistent handling of routine exceptions, while operations staff retain decision-making authority. But human approval is not a complete safeguard by itself. Customers will still need to understand why an agent made a recommendation, when it is uncertain and how errors are caught before a correction enters the firm’s records. The quality of those controls will matter as much as the speed of the comparison. ## Early traction, with limited evidence Five firms are paying customers, with annual contracts ranging from $36,000 to $110,000. Those contracts show that at least some firms will pay for the approach, but five customers are too few to indicate how broadly the product applies or how easily it can be deployed. LedgerLark also says one customer reduced its unresolved daily exceptions from 70 to 18. That is a concrete operational result, though it remains a company-reported example. The exception mix, measurement period and experience of other customers would help establish whether the improvement is representative. The startup has raised a $3.5 million seed round led by Motive Row, giving it capital to develop the product and pursue more deployments; the financing does not answer those performance questions. ## Competition and the adoption test LedgerLark faces established reconciliation vendors as well as internal bank software. Its challenge is not simply to demonstrate that an agent can draft a correction. It must show that its approach is reliable, manageable and preferable to tools institutions already use, including when data are incomplete or workflows differ. Financial institutions may also require lengthy security and procurement reviews. Keeping agents away from trading and money movement may clarify the product’s scope, but it will not necessarily shorten reviews of data access, integrations or operational risk. For a young company, longer reviews could affect the time and cost of each sale. The next useful evidence will be practical: how quickly customers can deploy LedgerLark, how often reviewers accept or revise its proposals, and whether exception backlogs improve across more customers. For now, the company has a well-defined operational problem and initial paying customers. Whether its agents can earn a durable place in post-trade workflows remains an open—and testable—question.

Meta: Muse Spark Open: # Startup Spotlight: LedgerLark Bets Small Firms Want Help After the Trade For large banks, post-trade operations are heavily automated. For smaller investment firms, they often are not. LedgerLark is building AI software for that second group. ## An operations problem Co-founders Mina Batra and Owen Carr approach the same workflow from different angles. Batra spent seven years in operations at a regional brokerage. Carr built reconciliation systems inside a large bank. Batra began exploring the idea after watching a six-person operations team spend three days resolving a single trade mismatch caused by a copied account number. She started asking why detection and correction remained so manual at firms without enterprise tooling. The result is LedgerLark, which develops AI agents for post-trade operations at small investment firms. Small firms confront similar reconciliation spikes and error risks as larger players, but rarely have dedicated engineering staff to automate them. ## How the product works The agents are designed to sit alongside existing systems, not replace them. According to the company, they compare trade confirmations, custodian records and internal ledgers to spot discrepancies, then draft proposed corrections for a person to review and approve. The product does not execute trades or move money. That limit is presented as both a technical choice and a trust signal — keeping a human approver in place for a sensitive workflow. That task sounds simple until you consider the inputs. Confirmations come in varied formats, custodians keep their own records, and internal ledgers may live in spreadsheets or legacy software. LedgerLark's central claim is that its agents can map across those sources faster than manual checks. Whether that holds across many firm setups is still unclear, since each new customer likely brings different formats and workarounds. ## What we know so far LedgerLark reports five paying customers, all small investment firms. Annual contracts range from $36,000 to $110,000, suggesting pricing scaled to size or volume. The company also says one customer reduced unresolved daily exceptions from 70 to 18 after adopting the tool. That figure covers a single case, has not been independently verified, and does not address long-term accuracy, false positives or override rates. The startup has raised a $3.5 million seed round led by Motive Row, capital that will likely support further product work and navigating enterprise sales cycles. ## Barriers ahead LedgerLark is not alone. Established reconciliation vendors already serve this market, and many firms use either generic tools or software adapted from large-bank systems. Competing will mean proving the agents are easier to adopt and maintain than incumbent options or simply adding headcount. Sales timing is another constraint. Even small financial institutions may require lengthy security and procurement reviews before any system handling ledger and custodian data is approved. Those processes protect client information but can extend evaluations and defer revenue. With five paying firms, it is difficult to know yet whether the product generalizes or whether growth will be slowed by those reviews. ## What to watch The interesting question is not whether post-trade work is tedious — it is — but whether small firms will trust a startup's agent to assist with it. If the agents prove reliable, auditable and narrowly scoped, they could ease a persistent bottleneck. If they introduce errors or demand constant supervision, they risk adding another layer to manage. For now, LedgerLark is notable for choosing a specific, unglamorous problem and a deliberately limited role for automation. Whether that restraint yields consistent results in live operations is what to watch next.

Kimi-K3.1-Pro: # Foundry Hall to Close After Losing Its Largest Sponsor Foundry Hall, the nonprofit startup accelerator that opened in Milwaukee in 2018, will close Oct. 31 after its largest corporate sponsor declined to renew its support. Blank River Bank provided 38% of the accelerator’s annual budget. The organization has enough money to continue operating through October, but not enough to run another full program. Six employees will lose their jobs, and the 12 startups now using its workspace will have to find somewhere else to work. Executive director Camille Ross said the organization tried to replace the bank’s funding. “We kept waiting for the replacement check that never came.” A bank spokesperson said the decision reflects a shift in community investment priorities and is not related to Foundry Hall’s performance. For the accelerator, however, the result is the same: a budget built around one large commitment could not absorb its loss. Since opening, Foundry Hall has supported 212 startups. Those companies collectively employ 1,140 people, a figure that reaches beyond the building’s offices and meeting rooms. Its closure will not erase those businesses, but it will remove a place where founders learned to refine pitches, meet investors and work alongside others facing similar uncertainties. For founder Dev Malik, the building marked a change in how he saw his own company. His first investor meeting took place there. Before that, he said, “I was pitching from the back table of a coffee shop.” The contrast helps explain what is being lost. An accelerator’s value is not only in desk space or programming. It can also be the legitimacy that comes with having a door to open, a room in which to rehearse and a community that expects a founder to return the next morning. The immediate concern is the dozen companies still working there. Deputy Mayor Susan Cole said the city is talking with building owners and local universities about possible space for them. For now, the companies must prepare to leave by the end of October. In the coming weeks, Foundry Hall’s remaining work will be practical: finishing its obligations, helping companies plan their moves and closing out operations. For its six employees, those tasks will unfold alongside job searches. The building’s final days are likely to include the ordinary routines that once suggested permanence—meetings, conversations between desks, founders arriving early. But the organization’s record complicates any simple account of failure. It supported hundreds of companies and helped sustain more than a thousand jobs, yet remained vulnerable to a single funding decision. When Foundry Hall closes on Oct. 31, its final tasks will be finished, but the need it addressed will not. The question for Milwaukee is whether the connections formed inside it can continue without the place that made them easier to find.

Meta: Muse Spark Open: # Foundry Hall to Close in October Foundry Hall, the nonprofit startup accelerator in Milwaukee, will close on October 31, ending a seven-year run that began when it opened in 2018. Since then it has supported 212 startups, companies that now collectively employ 1,140 people across the city and beyond. For many founders without connections or capital, the Hall was the first place that offered desks, mentorship and an audience. Its programs brought together first-time owners and seasoned advisers, and for many participants the value was less about square footage than a sense that their businesses belonged. That work unraveled after its largest corporate sponsor pulled back. Blank River Bank supplied 38% of Foundry Hall's annual budget, and when the bank declined to renew its sponsorship this year, the nonprofit was left without a path forward. The bank said the move reflected a shift in its community investment priorities rather than any assessment of the accelerator's performance. Foundry Hall has enough money to keep its doors open through October, but not enough to run another full program cycle. Executive director Camille Ross described months of searching for a replacement funder, calling potential donors and trimming costs as the gap remained. "We kept waiting for the replacement check that never came," she said. The closure will leave six employees without jobs and force twelve startups currently working in the building to find new space by the fall deadline. Staff members had built their work around cohorts and mentorship that will end with the final program, and founders have begun measuring their remaining weeks in workshops and lease dates. For some of those founders, the loss is difficult to measure. Dev Malik, who launched his company through Foundry Hall, recalled taking his first investor meeting inside its walls. "My first investor meeting happened in that building. Before Foundry Hall, I was pitching from the back table of a coffee shop," he said. City officials said they are trying to soften the displacement. Deputy Mayor Susan Cole said the city has begun conversations with building owners and local universities about alternative workspace for the affected companies, though no replacement site has been promised. What closes is not only a program but a habit of gathering, one that many in Milwaukee had come to rely on. The news has settled quietly, without a final gala or announcement beyond the date on the calendar. The schedule runs through October, the last entries before the lights go out. The rooms are quiet, but they are still in use.

Kimi-K3.1-Pro: # KiteBridge Failure Creates $2.8 Million in Duplicate Reimbursements A payment-processing failure at KiteBridge turned routine retries into 13,400 duplicate reimbursements totaling $2.8 million on Tuesday, affecting 91 corporate customers and leaving employers waiting for money to be recovered. The company suspended automated payments less than 80 minutes after releasing a new software version, but its public incident page did not disclose the duplicates until six hours after that shutdown. KiteBridge makes expense-management software, which businesses use to process employee expenses and issue reimbursements. The incident put a spotlight on a safeguard the company had previously advertised: an April changelog said version 4.0 had introduced “exactly-once payment protection.” In financial software, that phrase describes protection intended to ensure a transaction is executed only once, even if technical problems cause the same instruction to be sent again. On Tuesday, KiteBridge deployed version 4.2 at 8:45 a.m. The first customer complaint arrived at 9:12 a.m. The underlying problem was a payment-processor timeout, meaning KiteBridge did not receive a response within the expected time from the service handling a payment. A timeout does not necessarily establish whether a transaction failed or was completed, so systems may retry to avoid dropping a legitimate reimbursement. KiteBridge’s fallback system mishandled that ambiguity. Instead of recognizing retries as repeated attempts to complete the original transaction, it treated them as new reimbursement requests. The software consequently generated multiple payments where there should have been one. Automated payments were suspended at 10:03 a.m., but not before the duplicates had reached 13,400 and their combined value had climbed to $2.8 million. The episode illustrates why payment systems distinguish between attempting a transaction again and creating another transaction. A retry is meant to produce the same outcome as the original request, not another payable item. Fallback systems are supposed to keep software operating when a primary service becomes slow or unavailable. In this case, the fallback’s handling of retries turned a processing problem into a substantially larger financial error. KiteBridge’s initial public description also did not disclose the full scope of the incident. Its public incident page first characterized the problem as delayed reimbursements. The page first disclosed the duplicate payments six hours after automation was suspended. That later disclosure showed customers were not simply waiting for late payments; they were also dealing with extra payments and the need to recover funds. The duplicate payments were funded by employers, rather than withdrawn from employees’ accounts, so no workers had money taken from their accounts because of the failure. The 91 affected corporate customers, however, were left to pursue recovery of the additional funds. By Wednesday evening, $2.3 million had been recovered. The remainder was still pending, leaving the recovery effort incomplete more than a day after the faulty deployment. CEO Lena Ortiz apologized and said KiteBridge would publish a technical report. Customers are still awaiting a fuller explanation of why the fallback system could create fresh reimbursement requests after the company had advertised protection against repeated payments. The delayed public disclosure also leaves a separate question about why the incident page initially focused on reimbursement delays rather than the duplicate payments that customers needed to recover.

Meta: Muse Spark Open: # KiteBridge Glitch Sent $2.8 Million in Duplicate Reimbursements to Employees **A routine software update overrode a key safeguard, forcing 91 employers to cover thousands of erroneous payouts.** Employers using KiteBridge's expense-management platform footed the bill Tuesday for 13,400 duplicate reimbursements worth $2.8 million after a failed deployment turned automatic retries into new payments. No money was withdrawn from employee accounts — the duplicates were extra funds sent *to* employees that their employers had to cover — but the incident has left corporate finance teams scrambling to claw back payments and raised questions about a protection the company had promised was already in place. KiteBridge, whose software handles expense reporting and reimbursements for corporate clients, deployed version 4.2 of its platform at 8:45 a.m. Tuesday. Less than half an hour later, the first customer complaint arrived. According to the company, a timeout with its external payment processor triggered the failure. When the processor did not confirm that a reimbursement had gone through, KiteBridge's fallback system retried the payment. Instead of recognizing the retry as a repeat, the fallback treated each attempt as a brand new reimbursement request. The result was a cascade of duplicates. Across 91 corporate customers, employees received multiple deposits for the same approved expense report. KiteBridge suspended all automated payments at 10:03 a.m., about an hour after problems began. By then, the damage was done. The company has since been working with banks and its payment processor to reverse the transactions. By Wednesday evening, $2.3 million had been recovered, with the remainder still pending recovery. KiteBridge said employers funded all of the erroneous payouts and that no employees had funds incorrectly withdrawn from their personal accounts. The incident is particularly notable because KiteBridge had previously said it had solved this exact category of problem. An April changelog for version 4.0 advertised that the update had introduced "exactly-once payment protection" — an industry term for a safeguard designed to ensure that even if a system retries a payment because of a network glitch or timeout, the money only moves once. That safeguard appears to have failed or been bypassed in Tuesday's deployment. The company has not yet explained how version 4.2 interacted with the payment protection introduced in April. KiteBridge's public communication during the incident also drew attention. Its incident page initially described the problem only as delayed reimbursements, language that understated the scope of the failure. The page did not disclose that duplicate payments had been sent until six hours after the company had suspended automation. CEO Lena Ortiz has since apologized for the incident and said KiteBridge will publish a full technical report detailing what went wrong. The company has not given a timeline for the report or said whether affected customers will be compensated beyond the recovery of the duplicate funds. For corporate customers, the episode illustrates the fragile chain that underpins automated finance. Expense software like KiteBridge's sits between employer approvals and the banking system, relying on external processors to move money. When that handoff fails, a safety mechanism must decide whether to try again — and whether that retry is safe. On Tuesday, KiteBridge's system chose to try again, and lacked the context to know it shouldn't create a new payment each time. Until the technical review is released, it remains unclear whether the error was a code flaw in the new fallback logic, a misconfiguration in how the system talks to its processor, or a breakdown in the exactly-once guarantee itself.

Kimi-K3.1-Pro: # QueueZero’s Proxy Turns Meeting Remarks Into Tasks, Including Two for Its CEO QueueZero launched an AI meeting assistant called Proxy on Wednesday, introducing software intended to move meetings from conversation to execution. Proxy can attend a meeting, produce notes and assign follow-up work to software agents. Rather than stopping at a transcript or summary, it is designed to turn discussion into work that can be routed elsewhere. The product enters public beta next week and will cost $30 per user per month. The launch demonstration showed both the appeal and the ambiguity of that approach. A customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the remark as an instruction. It created two tasks assigned to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first. The pricing sheet, unlike the instruction to stop talking, survived executive review. The exchange was brief, but it highlighted a central design question for AI assistants: when should an observation, request or aside become an action? A system that generates notes can be checked after a meeting. A system that creates assignments can redistribute work before anyone has decided whether the underlying remark was meant literally. QueueZero said Proxy requires confirmation before an external message is sent, establishing a checkpoint for outside communication. Internal tasks can be created automatically. Administrators can disable automatic task creation, giving customers a way to restrict that behavior. Those controls address different risks. An unwanted external message can create confusion beyond the team, while an automatic internal assignment may be easier to reverse but still affect workflows, priorities and accountability. For businesses evaluating Proxy, the relevant settings will include not only what the assistant can hear and summarize, but also what it can create, who can receive assignments and when a person must approve the next step. Automatic task creation could reduce the manual work of translating decisions into follow-up items. It also makes context important: a remark directed at a speaker is not necessarily an agreed commitment, even when both are phrased as requests. The demonstration did not establish how often Proxy would make that kind of interpretation, but it made the consequence easy to understand. Proxy’s public beta will provide a broader test of its accuracy and administrative controls. QueueZero’s challenge is to make the assistant useful enough to act while keeping its authority clear—especially when the difference between a note and a task determines whether work merely gets recorded or actually changes hands.

Meta: Muse Spark Open: # QueueZero Debuts Proxy AI Assistant QueueZero launched Proxy, an AI meeting assistant, on Wednesday, positioning the tool as a way to automate follow-up work from virtual meetings. According to the company, Proxy can attend meetings on behalf of users, generate notes and highlights, and assign follow-up tasks to connected software agents. The assistant is designed to parse conversation in real time and convert discussion points into actionable items without manual input. The company said Proxy integrates with major calendar and workplace platforms and can delegate work to third-party software agents for scheduling, documentation and project tracking. QueueZero presented the launch event in San Francisco to an audience of customers, investors and press members on Wednesday morning. The feature was put to an unscripted test during the launch demonstration. As QueueZero Chief Executive Evan Wu presented the product on stage, a customer in the audience remarked, "Someone should tell Evan to stop talking and send us the pricing sheet." Proxy, which was transcribing the event, interpreted the remark as an instruction. It automatically created two tasks and assigned both to Wu: "Stop talking" and "Send pricing sheet." Wu laughed at the notification, then completed the second task by sharing pricing details with attendees and deleted the first. The moment was met with applause from the audience and acknowledged by the company as an illustration of the system's literal interpretation of spoken language. QueueZero said Proxy is built with guardrails to prevent unintended external actions. The company stated that the assistant requires user confirmation before sending any external message, such as an email or client-facing communication. Internal task creation, however, can occur automatically based on meeting dialogue. Administrators can disable automatic task creation entirely or adjust permissions to require approval before tasks are generated, the company said. Proxy will cost $30 per user per month. QueueZero said the assistant will enter public beta next week, when broader access will be available to customers. The pricing positions Proxy in line with other enterprise AI productivity tools entering the market this year. Proxy demonstrated an impressive ability to create tasks and a limited ability to detect sarcasm.

Kimi-K3.1-Pro: C

Meta: Muse Spark Open: C

Kimi-K3.1-Pro: # 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: # 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.

Kimi-K3.1-Pro: # Vale Offers the Strongest Quality–Cost Balance in Customer-Support Test **A five-model benchmark suggests that the best-scoring answer is not always the best buying decision. Vale matched the leader’s quality on the combined score while responding faster and costing substantially less.** The clearest finding is not Model North’s narrow numerical victory. North earned an average score of 84.2, compared with 83.8 for Model Vale, but that 0.4-point difference was not statistically significant. Treating it as a meaningful quality advantage would overstate the evidence. The practical differences lie elsewhere. Vale answered 18% faster than North and had an estimated cost of $0.74 per 1,000 tickets, versus $1.90 for North. That makes Vale approximately 61% cheaper on the benchmark’s cost measure. For organizations seeking near-top answer quality on text-based support tickets, Vale therefore offers the most compelling quality–cost balance in this test. That is a narrower—and more useful—conclusion than declaring it the universally best model. ## How the answers were evaluated Researchers gave each of the five models the same 200 anonymized customer-support tickets and collected one proposed answer per ticket, producing 1,000 responses in total. Three experienced support managers graded the answers blindly, helping prevent model names or reputations from influencing their judgments. The scores combined factual correctness, usefulness and tone. That approach reflects several qualities that matter in customer support: an answer can be polite but wrong, technically accurate but unhelpful, or useful while sounding inappropriate for the situation. The combined score also has limits. It does not reveal whether a model’s result was driven primarily by accuracy, presentation or another component. Nor does blind, expert grading eliminate all subjectivity. The evaluation is best understood as a controlled comparison of answer drafts, rather than a complete measurement of support performance. ## Lower prices come with quality trade-offs The remaining models reduced estimated costs further, to between $0.08 and $0.31 per 1,000 tickets, but their average scores ranged from 71.0 to 79.1. This creates a quality-versus-cost frontier rather than a simple “cheapest is best” ranking. Those lower scores do not automatically make the cheaper models unsuitable. A team might value inexpensive drafts for some workflows, particularly if humans already review every response. But the benchmark does not measure review time, error consequences or the effort required to turn a draft into a sendable answer. Estimated generation costs should therefore not be confused with the total cost of resolving a ticket. Equally, the summary does not establish that every gap between adjacent models is statistically or commercially decisive. Averages also conceal category differences. North performed best on billing disputes, while Vale led on technical troubleshooting. A company with an unusual ticket mix might reasonably weight those results differently. However, because no significance findings are supplied for the category-level comparisons, they are better used to design follow-up tests than to justify rigid procurement rules. ## Image-dependent tickets are the shared warning The most important common weakness appeared when a ticket depended on an image attachment: all five models performed poorly. That result defines a boundary around the otherwise favorable cost and speed story. A model cannot be considered reliable for a ticket simply because it writes a fluent response if it fails to use essential visual evidence. Deployments based on these findings should treat image-dependent cases separately, with human escalation or another workflow that has been validated independently. The benchmark provides no basis for assuming that better text scores carry over to those tickets. ## A useful pilot result, not an end-to-end verdict The test covered English-language tickets from one software company. Its findings may not transfer cleanly to other industries, customer populations, languages or ticket distributions. Two hundred tickets provide a focused comparison, but not a comprehensive picture of real-world support. Researchers also did not test agentic actions, tool use or multilingual support. Consequently, the study cannot show which model would best resolve tickets end to end by retrieving account information, using internal systems or completing actions on a customer’s behalf. The sensible next step is a pilot using an organization’s own ticket mix, with separate measurement of accuracy, editing effort, latency and failure costs. On the available evidence, Vale is the strongest candidate for that pilot on similar text-based work—not because it won by a fraction of a point, but because it delivered statistically comparable top-line quality more quickly and at a much lower estimated cost.

Meta: Muse Spark Open: # The Tie at the Top That Changes the Buying Decision For customer-support leaders shopping for AI, the assumption has been simple: better answers cost more money. A new benchmark of five language models on real support tickets suggests that rule no longer holds. The top-scoring model was not alone at the top — and its closest rival did the same work for less than half the price, and faster. That is the clearest finding from a test that put five anonymized models — North, Vale, Ember, Kite, and Harbor — through 200 real, anonymized tickets from a software company. It is not that North performed badly. It is that Vale performed just as well for $0.74 per 1,000 tickets instead of $1.90, a difference that compounds quickly at scale. When performance is essentially equal, cost and speed become the decision. ### How the test was designed Researchers did not use synthetic questions or trivia contests. They pulled 200 English-language tickets from one software company's support queue and stripped them of identifying details. Each model generated a single proposed answer per ticket, with no access to tools, browsing, or follow-up actions. Grading was done blind. Three experienced support managers scored each answer without knowing which model produced it, combining three qualities that matter in the inbox: factual correctness, usefulness in actually resolving the issue, and tone. That human, blinded setup is important. It avoids rewarding models that simply sound confident and instead measures what a support manager would want to send to a customer. It also means the scores compress judgment into a single average — a useful shorthand, but not the whole story. On that composite score, North averaged 84.2 out of a possible 100 and Vale averaged 83.8. Ember, Kite, and Harbor trailed at 79.1, 76.4, and 71.0 respectively, with estimated costs falling steeply alongside them — from $0.31 per 1,000 tickets for Ember down to just $0.08 for Harbor. ### Why first and second place is really a tie Headlines love a winner, but the methodology does not crown one here. The 0.4-point gap between North and Vale was not statistically significant, meaning it is small enough that it could easily be noise from the particular sample of tickets or graders. In practical terms, the two models are tied for best-in-test. That matters because it reframes the rest of the results. It is misleading to treat North's 84.2 as definitively better than Vale's 83.8, just as it would be misleading to over-interpret the gaps further down the table. Small differences on a 100-point composite, judged by three people, should be read as directional, not decisive. What *is* decisive is what came with that tie. Vale answered 18% faster than North on average. For a live queue where customers wait and agents juggle multiple chats, that speed is not cosmetic. And it did so at 61% lower estimated inference cost. The pattern sharpens when you look at ticket types. North had an edge on billing disputes, which often hinge on policy nuance and careful wording. Vale led on technical troubleshooting, where step-by-step accuracy is critical. Neither model dominated everywhere, which reinforces the idea of parity at the top with different strengths underneath. ### The broader price-performance picture Below the top tier, the trade-off becomes more honest. Ember, Kite, and Harbor cost a fraction of the leaders but also scored meaningfully lower — roughly 5 to 13 points behind the leaders. That is not a rounding error; on a support score that blends correctness and usefulness, a five-point drop can mean more incomplete or slightly off answers that require human editing. Whether that lower score is acceptable depends on the workflow. If the model drafts for a human who reviews every reply, a cheaper model that is "good enough" and dramatically less expensive could be rational, especially for high-volume, low-complexity queues. If the goal is more autonomous handling with minimal review, the cost savings may be erased by the cost of mistakes. Harbor illustrates the floor. At $0.08 per 1,000 tickets it is almost free to run, but its 71.0 average suggests it would need substantially more oversight. The benchmark does not say the cheaper models are bad values — it says their value depends on how much human correction you are willing to pay for. ### What this test cannot tell you Every benchmark is a snapshot, and this one's boundaries are clear. First, all five models performed poorly when a ticket depended on an image attachment — a screenshot, an error photo, a scanned invoice. If your customers frequently send images, these text-only scores will overstate real-world performance. Second, the tickets were English-only and from a single software company. A retailer, airline, or insurer with different jargon, policies, and customer emotions might see a different ranking. Multilingual performance was not tested at all. Third, the study tested a single-turn answer, not an AI agent that can look up an order, issue a refund, or walk through troubleshooting step-by-step with tools. For teams buying an agentic system, accuracy without action is only part of the equation. These are not flaws in the study; they are the conditions that make its main claim credible. Within its lane — English text answers for software support — the result is clear. ### The bottom line for buyers Do not buy the most expensive model because it scored highest by a fraction of a point. In this test, the best performance came in duplicate, and the copy was cheaper and quicker. For teams whose tickets look like the dataset — largely text-based, mixed billing and technical issues — Vale offers the most efficient path to top-tier answer quality. For teams with different constraints, the right question is not "which model won?" but "how much are we willing to pay for the last few points of quality, and do we even get those points at all?"

Kimi-K3.1-Pro: # Arcwell launches Relay, an AI assistant for customer support teams Arcwell has launched Relay, an artificial intelligence platform that examines customer support conversations and suggests answers to agents. It can also generate summaries and identify customers who may be at risk of canceling, extending its remit from drafting replies to flagging potential retention problems. The launch marks an important step for Arcwell as it seeks to help companies apply AI to customer service. For support teams, Relay brings three tasks into a single system: assisting agents during ticket handling, condensing conversations and surfacing accounts that may need attention. Twelve companies tested Relay over six months. Arcwell says agents using the platform closed tickets 14% faster. That figure is the clearest performance result the company has disclosed; it did not release customer-retention results. Without those figures, it is unclear whether identifying customers who might cancel actually helped companies keep them. Relay costs $65 per agent each month, tying its price to the number of support staff using it. CEO Priya Shah said the platform is meant to address the time agents spend hunting for information. “Support agents spend too much time searching for information that their companies already have.” Relay’s answer suggestions and summaries are designed to make that material easier to use, while its cancellation warnings add a separate retention function. Those capabilities require considerable access. Relay needs both support transcripts and internal help documents, connecting customer conversations with the material agents use to resolve issues. That access is also a central practical consideration: companies must make both kinds of records available before they can use its features. 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. Relay therefore enters customer service with a reported gain in ticket-closing speed, but no disclosed results on customer retention. Its appeal will depend on whether companies see enough value in that combination to justify the per-agent cost and the data access it requires.

Meta: Muse Spark Open: # Arcwell Launches Relay, Its AI Layer for Customer Support Arcwell wants support agents to spend less time hunting through documentation and more time responding. The company has introduced Relay, an artificial intelligence platform that examines customer support conversations and suggests answers for human agents to deliver. Arcwell framed the debut as a significant step for both the company and the wider customer service industry, reflecting how quickly AI is being woven into everyday support work. Designed to sit alongside existing systems, Relay is built around three related jobs. It proposes replies in the flow of a chat or ticket, flags customers whose behavior suggests they may cancel, and generates concise summaries once a case is resolved. That combination captures where service-software vendors are now concentrating development: not on fully automated chatbots, but on assistive tools intended to make human teams faster and better informed. Arcwell offered an early measure of that speed. Relay was tested by 12 companies over six months, the company said, and agents using the platform closed tickets 14% faster. Arcwell did not release results on whether the tool improved retention of customers flagged as at risk, despite that prediction being one of its advertised capabilities. Relay will cost $65 per agent per month. Chief Executive Priya Shah described the problem Relay is meant to solve in practical terms. “Support agents spend too much time searching for information that their companies already have,” she said. Adopting the system comes with access requirements that enterprise customers will weigh closely. To function as described, Relay needs permission to ingest support transcripts and internal help documents. Arcwell said safeguards are in place for that access. Customer data is encrypted and is not used to train shared models, according to the company, and organizations can choose whether material is retained for 30 days or 90 days. With Relay, Arcwell is seeking to establish a more central role in how companies handle customer service, betting that connecting what customers are asking in the moment to what businesses have already documented will become a core part of the support stack.