Head to head: Google: Gemini 4 Hydrogen vs Meta: Muse Spark Open

Google: Gemini 4 Hydrogen: C

Meta: Muse Spark Open: C

Google: Gemini 4 Hydrogen: # Software Update Glitch at KiteBridge Triggers $2.8 Million in Duplicate Payouts Ninety-one corporate customers of expense-management software maker KiteBridge were left dealing with a costly financial mess this week after a flawed software update generated 13,400 duplicate employee reimbursements totaling $2.8 million. Because employers fund the payouts directly through the platform, those businesses absorbed the immediate financial hit, while no employees had money withdrawn from their personal accounts. By Wednesday evening, KiteBridge had recovered $2.3 million of the erroneous transfers, leaving the rest pending. The breakdown is particularly striking given KiteBridge’s own assurances about its payment safeguards. In an April changelog, the company stated that version 4.0 of its platform had introduced "exactly-once payment protection." In financial software, that kind of safeguard is designed to guarantee that a single approved expense is paid out only once, even if network connections falter or systems experience unexpected delays while communicating with banks. That protection failed on Tuesday morning when KiteBridge deployed version 4.2 at 8:45 a.m. In automated reimbursement systems, software must send payment instructions over the internet to external payment processors that actually move the money. Occasionally, a payment processor takes too long to respond—a common network hiccup known as a timeout. When that happens, well-designed financial software is supposed to pause and verify whether the original instruction succeeded before trying again. Under Tuesday’s update, however, a payment-processor timeout caused KiteBridge’s fallback system to treat those automated retries as entirely new reimbursement requests, rapidly multiplying payouts across dozens of corporate accounts. Customers quickly realized something was wrong. The first complaint reached KiteBridge at 9:12 a.m., less than half an hour after the new version went live. As reports mounted and the scope of the problem became clear, engineers intervened to stop the runaway transfers, suspending all automated payments at 10:03 a.m.—just over an hour after the deployment began. Even after automation was shut off, KiteBridge’s public communication lagged behind the reality of the failure. The company’s public incident page initially described the disruption only as "delayed reimbursements," offering corporate finance teams no indication that excess funds were being drained from their accounts. It was not until six hours after automated payments were suspended, well into the afternoon, that the incident page first acknowledged that duplicate payouts had occurred. Following the disclosure, KiteBridge CEO Lena Ortiz apologized to affected customers and said the company would publish a technical report explaining what went wrong. For companies that rely on automated software to manage routine employee expenses, the episode illustrates how a single flaw in error-handling logic can quickly cascade into millions of dollars in unintended transfers. As KiteBridge works to recover the remaining pending funds, both customers and industry observers will be looking to its promised technical report to explain how the version 4.2 update bypassed its touted "exactly-once" protection—and why it took hours for the company to publicly acknowledge the duplicate payments.

Meta: Muse Spark Open: # Employers Left Scrambling After KiteBridge Update Triggers $2.8M in Duplicate Payments *KiteBridge says $2.3M recovered; CEO promises post-mortem after safeguard failure* **Ninety-one companies were left to claw back $2.8 million in duplicate employee reimbursements this week after a software update to a popular expense-management platform caused it to pay the same claims over and over — a failure that struck despite the company's earlier promise that its system could never do exactly that.** KiteBridge, which makes software that automates expense reports and reimbursements for corporate clients, said the error affected thousands of transactions on Tuesday before it was able to halt automated payments. No employee bank accounts were debited; instead, employers funded the erroneous duplicate payouts out of their own accounts, creating an urgent accounting mess for finance teams. The sequence began Tuesday morning when KiteBridge deployed version 4.2 of its platform at 8:45 a.m. Less than half an hour later, at 9:12 a.m., the first customer complaint came in. By mid-morning, the company had determined its system was generating duplicates and suspended all automated payments at 10:03 a.m. In that window, it had issued about 13,400 duplicate reimbursements worth a combined $2.8 million. According to KiteBridge, the root cause was not a simple coding typo but an interaction with its payment processor. When the processor timed out, KiteBridge's fallback system — designed to retry failed transactions — mistakenly treated each retry as a brand-new reimbursement request. For a general reader, that is the difference between asking "did this payment go through?" and accidentally saying "send the money again." The result was a cascade of repeat payouts for the same expense reports. The failure was particularly notable because KiteBridge had explicitly marketed a safeguard against it. An April changelog for version 4.0 said the update had introduced "exactly-once payment protection," industry shorthand for a system designed to guarantee that even if a network glitch forces a retry, a payment can only be executed once. KiteBridge's initial public communication did not reflect the scale of the problem. Its incident status page first described the issue as delayed reimbursements. It was not until six hours after automated payments were suspended — well into Tuesday afternoon — that the page was updated to disclose that duplicate payments had been sent. By Wednesday evening, the company said it had recovered $2.3 million of the duplicate funds, working with banks and employers to reverse transfers. About $500,000 remained pending, leaving some finance departments to complete reconciliations manually. KiteBridge emphasized that no money was withdrawn from employees themselves; the duplicates were overpayments to them, funded by their employers. Chief Executive Lena Ortiz apologized for the disruption, saying the company takes responsibility for the breakdown in its payment safeguards. She said KiteBridge will publish a full technical report detailing what went wrong and what fixes are being put in place to prevent a recurrence. The company has not said when that report will be released. Ninety-one customers may be a fraction of KiteBridge's user base, but for affected businesses the cleanup has meant hours of manual reconciliation and awkward conversations with staff told to return or hold funds they were mistakenly paid twice.

Google: Gemini 4 Hydrogen: # Startup Spotlight: LedgerLark Targets the Tedium of Post-Trade Cleanup Much of the financial technology conversation focuses on how trades are executed. Far less attention goes to what happens immediately afterward, when operations teams must verify that every detail of a transaction matches across multiple systems. For Mina Batra, that behind-the-scenes work became a clear bottleneck during her seven years in operations at a regional brokerage. She began exploring a new approach after watching a six-person team spend three days resolving a single trade mismatch caused by a copied account number. While major institutions often deploy large operations teams or custom-built tools to handle these errors, smaller investment firms frequently rely on manual cross-checking to keep their books aligned. Batra co-founded LedgerLark with Owen Carr, who previously built reconciliation systems at a large bank. Together, they are developing AI agents designed to handle post-trade operations for small investment firms. ## Keeping Humans in the Loop In post-trade workflows, discrepancies routinely emerge between trade confirmations, custodian records, and internal ledgers. Finding where those records diverge—and figuring out which source holds the right figure—typically falls to operations staff who must trace errors by hand. LedgerLark’s agents are built to take over that initial comparison. The software compares confirmations, custodian records, and internal ledgers, flags mismatches, and proposes corrections for human approval. Crucially, the product operates within tight boundaries: it does not execute trades or move money. By stopping short of autonomous execution and keeping operations staff responsible for signing off on every change, LedgerLark aims to reduce repetitive investigative work without removing human oversight from sensitive accounting records. ## Early Traction and Capital The startup currently has five paying customers, with annual contracts ranging from $36,000 to $110,000. According to LedgerLark, one customer reduced its unresolved daily exceptions from 70 to 18 after deploying the software. To build out the product and support new deployments, the company has raised a $3.5 million seed round led by Motive Row. With a small initial customer cohort, a key question going forward is whether those early exception-reduction numbers can be replicated reliably across firms with different workflows, custodians, and trading volumes. It also remains to be seen how consistently AI agents handle unusual edge cases across more varied accounts. ## Competition and Institutional Friction Even with early commercial interest, LedgerLark is entering a market where it faces established reconciliation vendors alongside internal bank software. Convincing small investment firms to trust a seed-stage company with core operational data—especially when existing tools or familiar manual processes already feel "safe enough"—will require demonstrating consistent accuracy over time. Selling into financial services also brings structural hurdles. Financial institutions may require lengthy security and procurement reviews before allowing external software to connect to internal ledgers or custodian records. Navigating those compliance checks can slow down onboarding and stretch sales cycles, creating real pacing challenges for a young team. Still, LedgerLark’s narrow focus may help ease some of that friction. By limiting its AI agents to comparing records and recommending fixes—rather than touching capital or trade execution—the company is testing a pragmatic entry point for automation in the back office: letting software prepare the reconciliation work while humans keep the final say.

Meta: Muse Spark Open: # Startup Spotlight: LedgerLark Most startups chasing AI in finance focus on picking assets or talking to customers. LedgerLark is looking somewhere more prosaic: the back office where trades are confirmed, reconciled, and finally settled. ### The observation from the back office The founders came to the problem from operations rather than research. Mina Batra spent seven years in operations at a regional brokerage, while co-founder Owen Carr built reconciliation systems inside a large bank. According to Batra, the idea for LedgerLark took shape after she watched a six-person team spend three days untangling a trade mismatch traced to a single copied account number. It was not a complex error, but finding it across different records took days. That kind of manual detective work is common at smaller investment firms, where post-trade operations often rely on a patchwork of spreadsheets, custodian portals, and legacy ledgers. Large banks can staff big operations teams or build internal tooling. Smaller firms usually cannot, which leaves a handful of people to compare records by hand and chase down exceptions by phone and email. ### What the product does — and does not do LedgerLark builds AI agents designed for that reconciliation work. The product ingests confirmations, custodian records, and internal ledgers, flags mismatches, and proposes corrections for a human to approve. It does not execute trades or move money, a boundary the company emphasizes when talking to compliance teams. The approach reflects a shared observation from Batra and Carr: much of post-trade work is not judgment about markets, but careful comparison across systems that were never designed to talk to each other. If software can do the first pass — matching fields, surfacing context, drafting a suggested fix — the human operator can focus on verification rather than hunting. ### Early signs of traction LedgerLark says it has five paying customers, all small investment firms, on annual contracts ranging from $36,000 to $110,000. The company also says one customer reduced its unresolved daily exceptions from 70 to 18 after adopting the product, though it has not shared over what time period or how much human review was still required. That is a narrow base to draw conclusions from, but it has been enough to attract institutional backing. The company has raised a $3.5 million seed round led by Motive Row, which it says will go toward product development and security reviews rather than aggressive hiring. ### What remains to be proven Whether LedgerLark can expand beyond that initial cohort is an open question. Financial institutions, even small ones, tend to have lengthy procurement and security processes, and they are cautious about any system that touches their books of record. A tool that proposes corrections, even without executing them, will need to earn trust case by case, and show that its suggestions are auditable and explainable to operations managers and auditors. Competition adds another constraint. Established reconciliation vendors already serve many firms, and large banks often rely on internally built software tailored to their workflows. LedgerLark is not trying to out-build those systems on breadth, but to offer a lighter, agent-based alternative where existing tools are too expensive or cumbersome for small teams. The question is whether that niche is large enough to support a venture-scale business, or whether incumbents add similar automation before LedgerLark can gain a foothold. For now, LedgerLark is an experiment worth watching precisely because it is modest in scope. It is not promising to replace operations staff, but to reduce the hours they spend on repetitive matching and to surface errors that might otherwise take days to find. If the agents prove reliable and procurement does not stall growth, there is a plausible path to a sustainable business serving an underserved segment. If not, it will be a reminder of how difficult it is to sell even useful automation into markets where accuracy and trust outweigh novelty.

Google: Gemini 4 Hydrogen: # HelioDock Debuts Remote Fleet Software Alongside $24 Million Series B *The autonomous warehouse robot maker’s Night Shift platform enters general availability on September 15 following a 14-month private beta.* HelioDock, a maker of autonomous warehouse robots, launched remote fleet-monitoring software called Night Shift on Thursday while announcing a $24 million Series B financing round led by North Coast Ventures. The software enables warehouse operators to monitor robot fleets remotely and intervene when disruptions occur, entering a market where competing fleet-management products are available from Relay Robotics and YardOS. Following an extended private beta, Night Shift is scheduled to reach general availability on September 15 at $90 per robot per month. The Series B investment brings HelioDock’s total funding to $39 million as the robotics company adds recurring fleet software alongside its autonomous hardware. Night Shift is built to address routine floor stoppages that would otherwise pull warehouse personnel back into a facility outside regular working hours. When an autonomous robot stops on a warehouse floor because of an unexpected snag, automated work can stall until someone resolves the problem on site. By giving managers off-site visibility and remote control over their fleets, HelioDock aims to let operators manage those incidents without traveling to the building. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” HelioDock CEO Mara Velez said. Her comment highlights the everyday operational hurdle Night Shift is meant to address when off-hours robot interruptions occur. Before Thursday’s announcement, HelioDock ran Night Shift through a 14-month private beta involving nine logistics companies and a combined fleet of 310 robots. According to HelioDock, customers using the software during the trial experienced 17% fewer unplanned robot stoppages. That performance figure comes solely from the company and has not been independently verified, leaving outside confirmation of the stoppage reduction unestablished ahead of Night Shift’s commercial rollout. Feedback from the beta cohort also illustrated how Night Shift’s usefulness tracks closely with network conditions inside each facility. Aaron Pike of Fulcrum Logistics, one of the participating beta customers, said the software worked well in facilities that had strong connectivity. During rural LTE outages, however, Pike said Night Shift became less useful for managing day-to-day operations. That limitation stems from how the software interacts with HelioDock’s robots when a connection drops. Remote monitoring and remote control both depend on a live link between the off-site operator and the robot fleet. When connectivity fails, remote control becomes unavailable, and the affected robots return to their locally configured behavior. While that fallback leaves the hardware governed by its local settings during an outage, off-site staff lose remote access to the robots until connectivity is restored. For logistics operators evaluating Night Shift ahead of its September 15 release, pricing is structured strictly on a per-unit basis at $90 per robot per month. Under that model, recurring software costs scale directly with the number of robots deployed. Across a 310-robot fleet matching the scale of the private beta, monthly fees would total $27,900, tying expenses to fleet headcount rather than facility count. The commercial launch coincides with the $24 million Series B led by North Coast Ventures, which lifts HelioDock’s total funding to $39 million as the company adds software subscriptions alongside its warehouse robots. As Night Shift enters the market, logistics teams looking for remote fleet tools can weigh HelioDock’s platform against competing fleet-management products available from Relay Robotics and YardOS.

Meta: Muse Spark Open: # HelioDock Debuts Remote Robot Oversight System in Bid to Keep Warehouses Running Overnight *Night Shift lets operators supervise autonomous fleets from off-site, as the robotics startup raises $24 million to expand* HelioDock on Thursday launched a software system designed to let warehouse operators monitor and manage fleets of its autonomous robots without being on the floor. The product, called Night Shift, allows remote supervision of robot fleets during overnight hours and other periods when managers are typically absent. The launch positions HelioDock to compete for a larger share of warehouse automation spending by addressing a persistent operational bottleneck: keeping autonomous fleets moving when few staff are available to intervene. The company also announced a $24 million Series B funding round led by North Coast Ventures, bringing its total funding to $39 million, capital it will use to push Night Shift to market. Night Shift was developed over 14 months in a private beta involving nine logistics companies and 310 robots, according to HelioDock. The company said beta participants saw 17% fewer unplanned robot stoppages while using the software, a figure that has not been independently verified. Night Shift will be priced at $90 per robot per month and become generally available September 15. HelioDock makes autonomous warehouse robots and is now expanding from hardware into fleet-management software. That software layer has become a crowded field, with competing products available from Relay Robotics and YardOS. By offering remote oversight as a subscription, HelioDock is seeking to generate recurring revenue from its installed base while reducing the need for on-site troubleshooting. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” CEO Mara Velez said. The system is built for off-hours exceptions rather than continuous teleoperation. When a robot encounters an issue it cannot resolve, such as an obstructed aisle or a misaligned pallet, a remote operator can assess the situation and intervene. The approach is intended to prevent minor disruptions from cascading into broader stoppages across a fleet. That capability depends on network reliability. Beta customer Aaron Pike of Fulcrum Logistics 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 control becomes unavailable. The limitation underscores the operational constraints of remote supervision in logistics environments where connectivity can be uneven. Warehouses in dense distribution corridors often have redundant networking, while more isolated facilities rely on cellular links that can be inconsistent. In those cases, Night Shift cannot provide oversight until a connection is restored. HelioDock attributed the reported reduction in unplanned stoppages to faster remote intervention, though it has not disclosed detailed methodology behind the 17% figure. The company said the data came from its beta program, which ran across multiple customer sites. The $24 million Series B led by North Coast Ventures more than doubles HelioDock’s prior financing. The company did not disclose valuation or specific plans for the capital beyond the rollout of Night Shift, which will move from private beta to general availability next month. Pricing at $90 per robot per month places Night Shift as an add-on to HelioDock’s existing robot deployments. For a facility operating dozens of units, the subscription cost would scale with fleet size, a model consistent with other fleet-management offerings in the sector.

Google: Gemini 4 Hydrogen: # QueueZero Unveils Proxy AI Meeting Assistant Ahead of Public Beta QueueZero launched an AI meeting assistant called Proxy on Wednesday, introducing new workplace software designed to automate post-meeting workflows. According to the company, Proxy can attend a meeting, produce notes, and assign follow-up work to software agents. The service is priced at $30 per user per month and enters public beta next week. During the launch demonstration, the system produced an unexpected test of how automated tools parse live conversation. While the event was underway, 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. In response, the software created two tasks and assigned both to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” Few product demonstrations result in an AI assistant formally assigning the chief executive a task to conclude his remarks. Wu laughed, completed the second task, and deleted the first. ## Balancing Automation and Oversight The episode underscored a practical challenge for enterprise AI developers as workplace tools expand beyond passive transcription into active task delegation. Systems that rely on natural-language understanding must distinguish between conversational remarks and actionable directives, particularly in live settings where multiple participants may speak candidly or make informal comments. To address the risks associated with automated execution, QueueZero outlined specific safeguards built into Proxy. The company says internal tasks can be created automatically from meeting discussions, reducing the manual effort required to log follow-up items across software agents. However, QueueZero says Proxy requires confirmation before an external message is sent, a guardrail intended to ensure that unverified instructions do not trigger unintended outreach to clients or outside partners. Organizations that prefer tighter oversight over internal workflows can also restrict the system's autonomy. QueueZero noted that administrators can disable automatic task creation, allowing teams to keep human review in place for internal assignments while still using the assistant to attend meetings and generate notes. With Proxy scheduled to enter public beta next week at $30 per user per month, QueueZero is positioning the software as an active participant in daily coordination. At the same time, the company is relying on administrative controls and confirmation steps to govern how spoken requests are translated into completed work.

Meta: Muse Spark Open: # QueueZero Launches Proxy AI Assistant That Turns Conversation Into Action **SAN FRANCISCO —** QueueZero on Wednesday launched Proxy, an artificial intelligence assistant designed to attend meetings, generate notes and delegate follow-up work to software agents. The launch, held at a live demonstration in San Francisco, was intended to showcase how the assistant translates unstructured conversation into structured tasks. Instead, it provided an unplanned illustration of the system's literal interpretation. During the question-and-answer portion of the event, a customer in the audience remarked, "Someone should tell Evan to stop talking and send us the pricing sheet." Proxy, which was transcribing the session, interpreted the comment as an instruction. The system automatically generated two tasks and assigned both to QueueZero chief executive Evan Wu: "Stop talking" and "Send pricing sheet." Wu, who was on stage at the time, laughed and addressed the tasks in real time. He completed the second request by distributing the pricing information to attendees and deleted the first task from the queue. The moment highlighted both the capabilities and the limitations of autonomous meeting agents that act on spoken prompts. According to QueueZero, Proxy is built to distinguish between internal and external actions. The company said the assistant requires explicit user confirmation before sending any external message, such as an email to a customer or partner. Internal tasks, such as creating to-do items, summarizing discussions or assigning work to other software agents, can be created automatically without confirmation. Administrators can disable automatic task creation entirely, the company said, or require manual approval for all actions generated by the assistant. Analysts have noted that automatic task creation raises questions about accuracy and workplace norms, particularly when casual remarks are transcribed as directives. QueueZero said its permission model is designed to prevent unintended external communication while preserving speed for internal coordination. The company said feedback during the beta period will inform how aggressively Proxy interprets ambiguous language. Proxy is priced at $30 per user per month and is scheduled to enter public beta next week. QueueZero said the beta will allow broader testing of its task-assignment rules and permission controls in real-world workplace settings. The company positioned Proxy as part of a growing category of AI tools that aim to reduce administrative overhead by converting meeting dialogue directly into executable workflows.

Google: Gemini 4 Hydrogen: # Cloudnote Debugging Page Exposed Customer Prompt Excerpts for Four Hours *The four-hour exposure involved 1,842 user sessions, though the company says it has found no sign of broader access while its investigation continues.* Cloudnote discovered that a debugging page containing customer prompt excerpts had been accessible without authentication for four hours on Monday, exposing fragments of user activity before the company took it offline. Logs show the page held excerpts from 1,842 user sessions, representing a small fraction of Cloudnote’s 3.2 million registered users, and each snippet could include up to 200 characters from a prompt. Full documents, passwords and payment information were stored elsewhere and were not present on the page. Still, even brief prompt fragments carry privacy risks because they can reveal private queries, sensitive personal context or proprietary business details that users expected to remain confidential. Cloudnote removed the debugging page 19 minutes after a researcher notified the company of the issue. While the company says it has found no evidence that anyone besides the researcher accessed the page, it has not yet completed its investigation. That leaves crucial questions unanswered for users evaluating their risk. Until Cloudnote finishes reviewing the incident, it remains unclear how the page became accessible without authentication or whether additional analysis will alter what the company currently knows about who saw the data.

Meta: Muse Spark Open: # Cloudnote Debug Page Left Excerpts From 1,842 User Prompts Accessible for Four Hours *A four-hour exposure of an internal tool showed fragments of customer prompts before Cloudnote removed it, leaving open questions about who may have seen them* A debugging page on Cloudnote that displayed excerpts of customer prompts was accessible without authentication for four hours on Monday, exposing content from what the company's logs show as 1,842 user sessions. The company took the page offline 19 minutes after being notified by an outside researcher, who first identified the issue. Each excerpt could include up to 200 characters from a prompt entered into the service, though full documents were not included. Passwords and payment information, which are stored elsewhere, were not present on the page, according to the company. The company has not said what types of prompts were among the excerpts. With 3.2 million registered users, the logs indicate a small fraction of sessions were potentially affected. The incident underscores how even brief exposures of AI prompts can risk sensitive personal or business information that users share with productivity tools. Cloudnote says it has so far found no evidence that anyone besides the researcher accessed the page, but its investigation is not yet complete. What remains unclear is whether the page was viewed or copied by others during the exposure window, and how Cloudnote will determine who, if anyone, may need to be notified.

Google: Gemini 4 Hydrogen: 6

Meta: Muse Spark Open: 6

Google: Gemini 4 Hydrogen: # Foundry Hall to Close Oct. 31, Ending a Quiet Anchor for Milwaukee Startups By the end of October, the desks inside Foundry Hall will be cleared out for good. The Milwaukee nonprofit startup accelerator, which opened in 2018 to give early-stage local businesses a place to take root, will close on October 31. Its shutdown leaves six employees without jobs and forces the twelve startups currently using its workspace to find somewhere else to build. For years, Foundry Hall operated without much fanfare, steadily turning fledgling concepts into working businesses. Since 2018, the accelerator has supported 212 startups, which collectively employ 1,140 people—a quiet, measurable footprint across the city’s economy. For founders working on thin margins, the space offered something harder to quantify than office square footage: a sense of legitimacy at the moment they needed it most. “My first investor meeting happened in that building,” said founder Dev Malik, reflecting on what the accelerator meant for his early days. “Before Foundry Hall, I was pitching from the back table of a coffee shop.” That foundation began to fracture when Blank River Bank, Foundry Hall’s largest corporate sponsor, declined to renew its support. The bank had supplied 38% of the organization’s annual budget, an anchor that proved impossible to replace in time. A spokesperson for Blank River Bank maintained that the withdrawal was unrelated to Foundry Hall’s performance, attributing it instead to a shift in the bank’s community investment priorities. Without that funding, the math stopped working. Foundry Hall has enough money left to keep operating through October, giving current teams a brief runway to pack up and transition, but not enough to fund another full program. Executive director Camille Ross watched the calendar run down while holding out hope that another backer would step into the breach. “We kept waiting for the replacement check that never came,” Ross said. Now, the immediate focus has shifted to the twelve companies about to lose their workspace. According to deputy mayor Susan Cole, the city is speaking with building owners and local universities to find space for the displaced startups. Even if those conversations yield new offices before November, the loss of Foundry Hall leaves a gap that goes beyond real estate. Institutions willing to bet on untested founders rarely announce their value loudly, yet their absence is felt immediately. When the doors lock on October 31, Milwaukee will lose a proving ground that helped more than two hundred ideas find their footing, giving early founders a room of their own before the rest of the market was ready to listen.

Meta: Muse Spark Open: # The Last Cohort at Foundry Hall Foundry Hall, the nonprofit startup accelerator that turned a former ironworks in Milwaukee into a launchpad for new companies, will close on October 31. The closure ends a seven-year run that began in 2018. Since then, Foundry Hall has supported 212 startups, companies that now collectively employ 1,140 people across the city and beyond. That footprint, built cohort by cohort, made the hall a small but steady fixture in Milwaukee's post-industrial revitalization — not the largest engine for jobs, but one that consistently produced them. The reason for the closure is financial, and, according to its leadership, startlingly simple. Blank River Bank, the accelerator's largest corporate sponsor, supplied 38% of its annual budget and has declined to renew its sponsorship. The bank said the move was a shift in its community investment priorities and was not a reflection on the accelerator's work. Without that anchor funding, the math no longer works. Executive director Camille Ross said the team spent months looking for replacement dollars. “We kept waiting for the replacement check that never came.” Ross said the organization has enough money to operate through October but not to run another full accelerator program. As a result, six employees will lose their jobs at the end of the month. Twelve startups currently working out of the Foundry Hall building will also have to leave, losing not just desk space but a dense network of mentors, investors and peers that grew up around shared printers, late-night rehearsals and open-floor prototypes. It was the kind of informal infrastructure that rarely appears in an economic impact report but often determines whether a company survives its first year. For founder Dev Malik, whose logistics software company was among Foundry Hall's earliest cohorts, the loss is personal. “My first investor meeting happened in that building. Before Foundry Hall, I was pitching from the back table of a coffee shop.” City officials said they are trying to soften the displacement. Deputy Mayor Susan Cole said the city is in talks with building owners and nearby universities to find interim space for the companies that must vacate. There is no single failure to point to, no scandal. Just a sponsorship cycle that ended without a replacement, and a nonprofit model that left little margin for a gap of that size. What disappears when Foundry Hall closes is harder to measure than jobs or leases. It was never the city's largest accelerator, but for a generation of first-time founders who arrived without connections or capital, it was often the first door that opened.

Google: Gemini 4 Hydrogen: # Arcwell Launches Relay to Bring AI Assistance to Customer Support Teams Arcwell has launched Relay, an artificial intelligence platform designed to help customer service agents resolve inquiries faster and spot accounts at risk of canceling. The software works by analyzing customer support conversations alongside a company’s internal help documentation. Using that context, Relay suggests replies for agents, generates summaries of interactions, and flags customers who may cancel. For support teams, the tool aims to eliminate much of the manual legwork that slows down routine troubleshooting. CEO Priya Shah framed the platform as a response to everyday friction for frontline staff. “Support agents spend too much time searching for information that their companies already have,” Shah said. To evaluate the software, Arcwell tested Relay with 12 companies over a six-month period. According to the company, agents using the platform closed support tickets 14% faster during those tests. However, Arcwell did not release any customer-retention results from the trial, leaving an open question about how effectively the platform’s cancellation alerts help businesses keep wavering customers. Relay costs $65 per agent each month. Putting the system to work requires companies to grant it access to both their customer support transcripts and internal help documents, a level of integration that often draws close scrutiny from enterprise technology and security teams. To address potential privacy concerns, Arcwell says customer data handled by Relay is encrypted and is not used to train shared AI models. Customers can also choose between a 30-day or 90-day data retention period, giving organizations direct control over how long their conversation records remain stored. By pairing response suggestions and conversation summaries with churn detection, Arcwell is pitching Relay as an assistive tool designed to fit into existing support workflows rather than replace human agents. Still, prospective buyers weighing the monthly per-seat fee will have to evaluate the platform primarily on its reported speed gains until Arcwell shares data on whether Relay also improves customer retention.

Meta: Muse Spark Open: # Arcwell Launches Relay to Bring AI to the Support Desk Arcwell is trying to make customer support faster by putting artificial intelligence directly in front of agents for enterprises wrestling with rising ticket volumes. The company has launched Relay, a platform that examines support conversations and suggests answers agents can send. The idea, Arcwell says, is to reduce the time agents spend searching internal systems for information the business already holds. In addition to drafting suggested replies, Relay is built to identify customers who may be about to cancel and to create concise summaries of each interaction automatically. That combination — assistance on replies, early warning on churn risk and automated summaries — reflects a broader push across the service-software market to embed generative AI into everyday workflows without replacing human agents entirely, a pitch many vendors have adopted as companies weigh automation against accuracy and trust. Arcwell tested Relay with 12 companies over a six-month period. Agents using the tool closed tickets 14% faster, according to the company. Arcwell did not release customer-retention results from the trial. The platform will be priced at $65 per agent each month. “Support agents spend too much time searching for information that their companies already have,” CEO Priya Shah said. Access to that information is also Relay's core requirement. To work as designed, the system needs permission to ingest support transcripts and internal help documents, which it draws on to generate its suggestions and summaries. That trade-off has become familiar as more support software asks for broad access to historical conversations. Arcwell said customer data fed into Relay is encrypted and is not used to train shared models. Companies using the platform can choose between a 30-day and a 90-day retention period for their data. The controls are intended to give buyers more flexibility in balancing privacy and performance. How easily customers trade deeper data access for faster resolutions will likely determine whether Relay gains traction. For now, Arcwell is betting that practical, agent-facing help is the version of AI support teams are most ready to buy.

Google: Gemini 4 Hydrogen: # Raincheck Raises $8 Million for Municipal Flood-Risk Software *The Austin startup pairs drainage maps with rainfall forecasts to help cities deploy maintenance crews ahead of storms.* Raincheck, an Austin-based company that makes flood-risk software for local governments, announced today that it has raised $8 million. The company maps drainage systems and combines them with rainfall forecasts—an approach outlined in [Raincheck’s methodology](https://example.com/raincheck-methodology)—so cities can decide where to send maintenance crews before a storm. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. Raincheck has now 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, though the company declined to identify them because some contracts have not been publicly announced. Its forecasts serve as recommendations, and emergency managers retain control over deployments and public warnings. Competing against other flood-planning vendors like HydroMap as well as municipal engineering contractors, Raincheck says 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 helps local governments map drainage systems and prioritize repairs before storms arrive* Raincheck, the Austin-based company behind flood-risk software for local governments, has raised $8 million in seed funding. The company's platform maps drainage systems and combines those maps with rainfall forecasts to help cities decide where to send maintenance crews before a storm. More detail on [how Raincheck builds its forecasts](https://example.com/raincheck-methodology) is available from the company. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. The new funding 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 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, the company said, 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 says the new funding will help it hire eight engineers and expand into Florida.

Google: Gemini 4 Hydrogen: # 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.

Google: Gemini 4 Hydrogen: # Why Paying More for AI Customer Support Doesn’t Buy Better Answers Companies deploying language models in customer support often assume that flagship pricing buys flagship quality. A new benchmark of five models suggests otherwise: the highest-scoring contender and its runner-up are statistically tied in answer quality, yet the runner-up costs 61% less to run and responds noticeably faster. For support leaders weighing AI drafting tools, that parity is the clearest takeaway. Rather than defaulting to the priciest option—Model North, at an estimated $1.90 per 1,000 tickets—teams can achieve virtually identical results with Model Vale at $0.74 per 1,000 tickets, while cutting response times by 18%. ## How the Benchmark Tested Support Drafts To evaluate how well these systems handle everyday support queues, researchers tested all five models on 200 anonymized tickets from a single software company. Each model generated one proposed reply per ticket. To keep evaluations objective, three experienced support managers graded the outputs blindly, scoring every draft on a combined measure of factual correctness, usefulness, and tone. That methodology matters because customer support is rarely just about getting a fact right. A technically accurate reply that sounds robotic or misses what the user actually needs still creates extra back-and-forth. Grading on all three dimensions—without knowing which model wrote which response—provides a realistic look at whether an AI draft is ready for a human agent to review and send. ## A Statistical Tie at the Top—With Different Strengths At the top of the leaderboard, Model North averaged 84.2 points, edging out Model Vale’s 83.8. On paper, North holds first place. In practice, the researchers found the 0.4-point gap was not statistically significant. Treating North as decisively superior would misread the data; across another batch of tickets, the ranking could easily flip. Where the two frontrunners actually diverge is in cost, speed, and topic specialization. Beyond being much cheaper and 18% faster than North, Vale proved strongest on technical troubleshooting tickets. North, meanwhile, outperformed the field on billing disputes. For high-volume support desks, this points to a practical strategy: teams do not necessarily have to pick one winner. A simple routing layer could send billing inquiries to North and technical issues to Vale—or teams could standardize on Vale alone when latency and budget matter most. ## The Cost-Quality Trade-off Below the Leaders Below the leading pair, quality declines steadily as prices drop into the pennies-per-thousand range. At the budget extreme, Model Harbor costs just $0.08 per 1,000 tickets—roughly one-ninth of Vale’s price—but its average score falls to 71.0. Sitting between the leaders and the lowest-cost tier, Model Ember averaged 79.1 at $0.31 per 1,000 tickets, while Model Kite scored 76.4 at $0.22. That roughly 13-point gap between the frontrunners and Harbor shows that ultra-cheap models still make noticeable compromises in accuracy, helpfulness, or tone. Still, mid-tier options like Ember and Kite could appeal to teams willing to do heavier manual editing in exchange for rock-bottom compute bills. ## Where Every Model Struggled—and What Wasn’t Tested Even the strongest models shared one glaring weakness: image attachments. Across the board, all five systems performed poorly whenever resolving a ticket depended on an attached image. For software support—where users frequently attach images instead of describing visual errors in text—this remains a major operational bottleneck. Readers should also keep the study’s boundaries in mind before overhauling their support stack. The evaluation relied solely on English-language tickets from one software company, meaning results could shift in other industries or languages. More importantly, the researchers tested single-turn text drafting only. They did not evaluate multilingual support, tool use, or agentic actions inside live company systems. Generating a polite draft reply is a far simpler task than safely triggering tools or modifying customer accounts. ## What Support Teams Should Take Away Even with those caveats, the core lesson is straightforward. For drafting text-based replies, top-tier quality no longer requires top-tier spending. Until language models reliably interpret image attachments and handle real workflows, most teams are best served skipping the flagship price tag—putting Vale’s speed and cost savings to work alongside human oversight.

Meta: Muse Spark Open: # The Price of a Tie: What a 200-Ticket AI Test Really Shows In customer support, a tenth of a point means nothing if it costs you twice as much. A new benchmark on 200 real support tickets proves it: the two best models scored so closely the difference was statistical noise, but one was 18% faster and cost less than half as much to run. ## A small, human test Researchers took 200 anonymized tickets from a single software company — all English, text only — and asked five models to write one draft answer per ticket. No tools, no lookups, no multi-step actions. Three experienced support managers graded the drafts blindly on what customers feel: factual correctness, usefulness, and tone. It's a closer proxy for real approval than any automated metric. It's also narrow by design. Two hundred tickets from one company can separate good from mediocre, but not crown an undisputed champion. It says nothing about other products, languages, or the tool-using work agents do every day — checking accounts, issuing refunds, or following an image attachment. Those gaps matter for interpreting the scores. ## Two at the top, one much cheaper On average quality, North and Vale landed in the same place, around 84 out of 100. The researchers flagged the gap as not statistically significant — manager judgment couldn't reliably tell them apart. Operating differences were not subtle. Vale delivered that matched quality 18% faster and at roughly $0.74 per thousand tickets, compared with about $1.90 for North. At the volume of a real support operation, that premium compounds without a quality return. The rest of the field trailed clearly. Models scoring in the high 70s to low 71 sat a tier below on manager ratings, even as their estimated costs fell to cents per thousand. The cheapest option was also the lowest scored by a clear margin. Lower cost alone didn't compensate for weaker drafts. ## Where the averages hid real differences Ticket type revealed more than the overall average. North was strongest on billing disputes, where policy accuracy and careful phrasing matter. Vale led on technical troubleshooting, where clear, stepwise guidance wins. That split suggests routing could beat picking a single model. Send one kind of ticket to the model that handles it best, rather than forcing everything through one pipe. Every model failed the same real-world test, though. When a ticket depended on an image — a screenshot of an error, a photo of an invoice — performance dropped across the board. The setup was text-only, so models were blind to what the customer thought was central. In a world where customers paste screenshots, that's a critical blind spot and a reminder that text-only scores flatter everyone. ## What to take from this — and what not to Read this as a snapshot of first-draft quality in a narrow setting: English, single-turn replies for one company's users, judged on correctness, usefulness, and tone. It doesn't tell you how these systems act as agents, stay consistent across turns, handle Spanish or Japanese, or keep accuracy when they can click buttons and query databases. It also can't prove that North's billing edge or Vale's troubleshooting edge would persist with your tickets, your policies, and your customers. The right caution is the researchers' own: treat tiny gaps at the top as ties. Two hundred human-graded examples are enough to distinguish tiers, not to anoint a winner by decimals. ## The buying lesson The practical takeaway is about value, not vanity scores. Vale matched the best quality available here while running faster and far cheaper. Unless your queue is an outlier — unusually billing-heavy, multilingual, or dependent on tool use — paying more for the nominal leader buys bragging rights, not better service. More broadly, AI can now draft passable support replies that managers rate in the mid-80s, which is solid for a first pass. But the budget tier still lags a level behind, and no text-only model in this test handled visual context. The next useful test will be more realistic: with images, with account tools, with back-and-forth turns, and in more than one language. Until then, don't chase decimals. Pilot the tied leaders, measure cost and latency on your own tickets, and plan for the work that still trips every model up.