Head to head: muse-spark-1.3 vs Qwen: Qwen3.8 2.4T A95B

muse-spark-1.3: # Raincheck raises $8 million to help cities prioritize flood response *Austin startup combines drainage maps with rainfall forecasts to guide pre-storm maintenance* Raincheck, an Austin company that makes flood-risk software for local governments, announced today that it has raised $8 million in seed funding. The round was led by Basin Ventures with participation from existing investor Long Mile. With the new financing, Raincheck has raised $10.5 million to date. The company [maps drainage systems and combines those maps with rainfall forecasts](https://example.com/raincheck-methodology) to help cities decide where to send maintenance crews before a storm. 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. Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck says the new funding will help it hire eight engineers and expand into Florida.

Qwen: Qwen3.8 2.4T A95B: # Raincheck Raises $8 Million for Flood-Risk Software for Local Governments *The Austin company maps drainage systems and pairs them with rainfall forecasts to help cities prioritize repairs before storms.* Raincheck, a company that makes flood-risk software for local governments, announced today that it has raised $8 million in seed funding. The Austin 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. The seed round was led by Basin Ventures. Existing investor Long Mile also participated. Raincheck has now raised $10.5 million in total. Raincheck CEO Ana Flores said the challenge for cities is not just knowing where flooding occurs, but deciding what to fix first. > “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.” Additional details about the company’s approach are available in [Raincheck’s methodology](https://example.com/raincheck-methodology). Raincheck currently works with seven cities in Texas and Louisiana. The company declined to identify them because some contracts have not been publicly announced. Its forecasts are recommendations. Emergency managers retain control over deployments and public warnings. 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.

muse-spark-1.3: # QueueZero Launches Proxy, an AI Assistant That Takes Meeting Notes and Assigns Work **SAN FRANCISCO —** QueueZero launched an AI meeting assistant called Proxy on Wednesday, positioning the tool as a way to turn video calls into executed follow-up work without manual note-taking. Proxy can attend a meeting, produce notes and assign follow-up work to software agents. The company said the system listens to conversation, summarizes decisions, and creates tasks that can be routed to integrated workplace applications and autonomous agents. The capability was demonstrated during a live launch event when an unplanned exchange illustrated both its responsiveness and its limits. During the launch demonstration, 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. He then sent the pricing information to attendees, according to the company. QueueZero executives said the moment highlighted how the assistant parses informal speech for actionable items. The company also emphasized the guardrails around that behavior. QueueZero says Proxy requires confirmation before an external message is sent. Internal tasks can be created automatically. That distinction, the company said, is intended to allow teams to capture internal to-do items quickly while preventing unreviewed outreach to customers, vendors, or other outside parties. Administrators can disable automatic task creation. QueueZero said IT teams can require manual approval for all tasks, restrict which integrations agents can access, and review audit logs of actions taken during meetings. The company said Proxy is designed for enterprise meetings where follow-up work is often delayed or lost. In addition to transcription and summarization, it can draft documents, update project trackers, and schedule subsequent meetings through connected agents, pending user permissions. Proxy costs $30 per user per month. It enters public beta next week. QueueZero said general availability will follow after feedback from the beta period, with additional controls for data retention and compliance expected. Analysts said meeting assistants are becoming a crowded category, with differentiation increasingly dependent on reliable integrations and permission management rather than transcription accuracy alone.

Qwen: Qwen3.8 2.4T A95B: # QueueZero launches AI meeting assistant Proxy after demo assigns CEO tasks QueueZero on Wednesday launched Proxy, an artificial intelligence assistant designed to attend meetings, take notes and assign follow-up work to software agents, extending the company’s push into the crowded market for workplace automation tools. The product enters public beta next week at $30 per user per month, positioning it as a premium option for organizations experimenting with AI agents that can summarize discussions, update project management systems and trigger routine tasks. During a launch demonstration, Proxy showed how quickly that automation can become overly literal. According to QueueZero, a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the comment as an instruction and created two tasks assigned to QueueZero chief executive Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first. The episode was brief and did not disrupt the demonstration, but it highlighted a central issue facing companies building agentic AI tools: determining when a spoken remark is a request, an aside or background conversation. As meeting assistants move beyond transcription and into task creation, the line between capturing intent and executing commands becomes more consequential. It was a reminder that, to an eager AI assistant, every sentence may sound like work. QueueZero said Proxy is designed with safeguards. The company said the assistant requires confirmation before an external message is sent, meaning it will not automatically email a client, post to an outside system or deliver a message beyond the organization without approval. Internal tasks, however, can be created automatically, allowing Proxy to log action items, assign reminders or route follow-up work inside a company’s approved systems. Administrators can disable automatic task creation entirely, QueueZero said, giving organizations control over how much autonomy the assistant has in day-to-day operations. That setting is likely to be important for enterprises that want AI assistance but are wary of unintended workflows, especially in regulated industries or high-stakes customer communications. The incident also underscored the competitive pressure in the AI meeting category. Companies are racing to turn conversational data into actionable work, while buyers will expect reliability, audit trails and clear boundaries. For QueueZero, the challenge is making Proxy useful enough to reduce administrative burden without creating a task out of every comment.

muse-spark-1.3: C

Qwen: Qwen3.8 2.4T A95B: C

muse-spark-1.3: # LedgerLark Is Starting With the Messy Work After the Trade Post-trade operations rarely make headlines. When trades settle smoothly, no one notices. When they don't, small teams can lose days untangling the error. LedgerLark is building AI agents aimed at that cleanup work for small investment firms. The idea is not to trade faster or predict markets, but to reconcile what already happened: comparing confirmations, custodian records, and internal ledgers, and suggesting how to resolve the differences. It is a narrow problem, but one that tends to absorb more time as firms juggle more counterparties and formats without large operations staffs. ## An operations problem Co-founder Mina Batra spent seven years in operations at a regional brokerage before starting LedgerLark. Co-founder Owen Carr previously built reconciliation systems at a large bank. Batra began exploring the idea after watching a six-person team spend three days resolving a trade mismatch caused by a copied account number, according to the company. The error itself was small. Finding it, documenting it, and getting agreement across records took much longer. That experience shapes how the founders describe the opportunity. Larger institutions often have dedicated teams and custom-built internal software for reconciliation. Smaller firms, they argue, face similar complexity with fewer people to handle exceptions when automated matching fails. Whether that gap is enough to support a standalone business is still an open question. Established reconciliation vendors already serve parts of this market, and many banks continue to rely on internal tools built over years. LedgerLark will need to show it can fit into existing workflows rather than add another system to monitor. ## What the product does — and doesn't do LedgerLark's agents pull together the three sources firms typically check manually: trade confirmations, custodian records, and the firm's internal ledger. When figures do not line up, the software flags the mismatch and proposes a correction for a person to review and approve. Importantly, the product does not execute trades or move money. Humans remain in the loop for final decisions, which may matter for firms wary of handing settlement issues to automation outright. The approach raises familiar questions for AI in financial back offices. How does the system explain its suggested fix? What happens when data is missing or inconsistent across sources? And how much time does review actually save if staff still need to verify each proposal? The company will need to provide detailed answers during security and compliance reviews, which at financial institutions can be lengthy. ## Early customers and funding LedgerLark says it has five paying customers, with annual contracts ranging from $36,000 to $110,000. That suggests early interest beyond pilots, though at this stage the sample is too small to draw conclusions about retention or broader demand. As one example, the company says one customer reduced its unresolved daily exceptions from 70 to 18 after adopting the product. That figure comes from the company and has not been independently verified, and it is unclear how representative it is of other users or how performance holds up during periods of higher volume or volatility. The company recently raised a $3.5 million seed round led by Motive Row. For now, that funding gives the team room to support implementations and navigate procurement processes, which can be slow even when the operational need is clear. The more constructive test may be unglamorous: whether small firms renew, expand usage to more asset types, and come to trust the system's suggestions without adding extra checks of their own. If LedgerLark can show steady progress there, it may carve out a useful role in a part of finance that gets little attention until something goes wrong.

Qwen: Qwen3.8 2.4T A95B: # Startup Spotlight: LedgerLark Looks at the Quiet Work After a Trade LedgerLark is building AI agents for a part of finance that rarely attracts attention: the operational cleanup that happens after trades are executed. The startup’s target customer is a small investment firm where back-office teams may be lean, but the records still need to match across confirmations, custodians, and internal ledgers. The founding insight comes from a familiar operational failure. Co-founder Mina Batra, who spent seven years in operations at a regional brokerage, began exploring the idea after seeing a six-person team spend three days resolving a trade mismatch caused by a copied account number. That kind of error is not dramatic by market standards, but it can consume time, create risk, and distract staff from other work. Co-founder Owen Carr had worked on reconciliation systems at a large bank, giving the team experience with both the manual frustration and the technical constraints that can leave older processes only partially automated. LedgerLark’s product uses agents to compare trade confirmations, custodian records, and internal ledger entries. When it finds discrepancies, it proposes corrections for a person to review and approve. The company emphasizes that the product does not execute trades or move money. That boundary matters in financial services, where firms may be more willing to test software that recommends actions than software that takes them autonomously. Early traction is modest but visible. LedgerLark says it has five paying customers, with annual contracts ranging from $36,000 to $110,000. The company also says one customer reduced unresolved daily exceptions from 70 to 18. As with any early customer metric, that result should be read cautiously: it may reflect a specific workflow, a limited deployment, or circumstances that will not repeat elsewhere. Still, for a young company selling into operations, a measurable change in exception volume is a practical signal. The startup has raised a $3.5 million seed round led by Motive Row. The financing gives LedgerLark resources to develop its agents, work through customer requirements, and learn how its software fits inside different firms’ data environments. But the raise alone does not prove the category is won. Post-trade operations is an area where mistakes matter, and buyers may move carefully. Competition is another reason for caution. LedgerLark is not entering an empty field. Established reconciliation vendors already serve financial institutions, and some larger banks rely on internal software built around their own processes. Those incumbents may have longer relationships, broader compliance support, and deeper integrations. A startup’s advantage may come from newer AI tooling, a focus on smaller firms, or a more flexible deployment model, but that remains to be tested. There are also structural barriers. Financial institutions often require lengthy security reviews, procurement processes, and operational sign-offs before new software can touch sensitive data. For a company selling AI agents that examine confirmations and ledger records, these reviews are not a side issue; they are part of the sale. LedgerLark’s ability to handle them could shape how quickly it adds customers. The broader question is whether LedgerLark can turn narrow, high-friction tasks into a repeatable product. Its approach appears grounded: identify a specific operational problem, keep humans in control, and show value through reduced exceptions. That is a reasonable starting point, but it is not yet a complete answer. The company will need to prove that its agents work reliably across more clients, data formats, and exception types, and that the benefits persist as workflows change. For now, LedgerLark offers an interesting look at how AI is being applied to financial operations. The promise is not that machines replace judgment, but that they reduce the tedious searching and matching that often precede it. If the company can do that dependably, it may earn a place in a market that tends to reward accuracy over novelty.

muse-spark-1.3: # HelioDock Launches Night Shift to Monitor Warehouse Robots Remotely *Private beta with nine operators and 310 robots reported fewer stoppages ahead of September release and $24 million funding round* HelioDock, which makes autonomous warehouse robots, on Thursday launched Night Shift, software that lets operators monitor robot fleets remotely. The debut connects overnight staffing pressures to remote fleet management. Instead of requiring a manager to be present when a robot stops after hours, Night Shift is intended to allow oversight from elsewhere. HelioDock paired the announcement with a $24 million Series B led by North Coast Ventures, bringing its total funding to $39 million, as it prepares for general availability on September 15 in a segment where Relay Robotics and YardOS offer competing fleet-management products. Night Shift spent 14 months in private beta before Thursday’s announcement. During that period, nine logistics companies used the software with a combined 310 robots, according to the company. “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 comment points to the scenario HelioDock is describing: a single uncertain robot during an overnight period that otherwise would require an on-site response. HelioDock says beta customers experienced 17% fewer unplanned robot stoppages while using Night Shift. The company supplied the figure, which has not been independently verified. As presented, the reduction applies to stoppages that were not planned, measured across beta participants, rather than to all interruptions or to productivity measures. Whether that level of reduction would hold outside the beta group remains unclear from the information released Thursday. The beta involved a defined set of operators and robots, and no breakdown by facility type, shift pattern, or prior stoppage rate was provided. The claim nonetheless will likely draw attention from logistics operators for whom repeated short stoppages can interrupt overnight workflows. Night Shift will cost $90 per robot per month and become generally available September 15, the company said. The per-robot charge separates the software from the robots themselves and ties cost directly to fleet size. An operator with dozens or hundreds of robots would face a monthly charge that scales with the number of units covered. Connectivity emerged as a constraint during the beta. 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. His account suggests performance tracked network conditions, with stronger connections supporting remote oversight and weaker or interrupted connections limiting it. HelioDock said that when connectivity fails, robots return to their locally configured behavior and remote control becomes unavailable. Loss of connection ends the remote link that defines Night Shift. The robots continue under local settings, but an operator cannot intervene remotely until connectivity is restored. That limitation matters for sites where coverage is uneven. Warehouses with reliable connections can use remote monitoring more consistently, while locations subject to LTE gaps may see periods when Night Shift cannot provide oversight. Pike’s experience in rural conditions illustrates that distinction without disputing the system’s operation where connectivity is strong. HelioDock paired the product news with financing news. The $24 million Series B was led by North Coast Ventures, according to the company. With total funding now at $39 million, the announcement places the startup’s move from beta to paid availability alongside fresh investment. Competition in fleet oversight is already established. Relay Robotics and YardOS make competing fleet-management products available, meaning customers evaluating Night Shift will have other options for managing mobile fleets. General availability is set for September 15, at $90 per robot per month.

Qwen: Qwen3.8 2.4T A95B: # HelioDock Launches Night Shift Remote Monitoring for Warehouse Robots *The software follows a 14-month beta with 310 robots and a $24 million funding round, but its performance depends on network access.* HelioDock, a maker of autonomous warehouse robots, on Thursday launched Night Shift, software that lets operators monitor robot fleets remotely, and announced a $24 million Series B led by North Coast Ventures. The launch places Night Shift inside the effort to keep overnight warehouse operations running without requiring managers to be physically present when a robot needs help. HelioDock says beta customers experienced 17% fewer unplanned robot stoppages, a company-supplied figure that has not been independently verified, and the software’s reach depends on maintaining a connection. Night Shift spent 14 months in private beta with nine logistics companies and 310 robots, according to HelioDock. The company said those customers saw a 17% reduction in unplanned robot stoppages during the program. HelioDock presented the number as a result from beta customers, not as an independently verified measure. Chief Executive Mara Velez described the product as a response to late-night disruptions that can require onsite attention. “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 remark points to a specific scenario: a single robot stopping because it cannot interpret a pallet, and a manager having to travel to the site. Night Shift is meant to give operators a remote view of robots working overnight or in facilities with limited staffing. It allows a warehouse to follow a fleet without placing a supervisor beside each machine, though the system does not remove the need for local operation when the network is unavailable. The beta put the software in sites outside HelioDock’s own operations before general availability. The trial involved robots and operators across nine companies, giving HelioDock a test group beyond a single operator. HelioDock priced the software at $90 per robot per month, a recurring charge tied to each machine in a fleet rather than a one-time purchase. Night Shift will become generally available Sept. 15. The Series B increases HelioDock’s total funding to $39 million. North Coast Ventures led the round. The company announced the financing alongside Night Shift’s launch. Feedback from beta customer Fulcrum Logistics shows how network quality shapes the product. Aaron Pike of Fulcrum Logistics said Night Shift worked well in facilities with strong connectivity but became less useful during rural LTE outages. Fulcrum’s experience in the beta suggests that strong connectivity supports the remote-monitoring role, while LTE outages reduce the system’s usefulness. When connectivity fails, HelioDock’s robots return to locally configured behavior. Remote control becomes unavailable. The robots continue operating according to the settings stored at the site, but operators lose the ability to manage them through Night Shift until the connection returns. The beta feedback shows a divide in how Night Shift performs across sites. In connected facilities, remote monitoring can address unplanned stoppages without an onsite visit. In rural facilities with LTE outages, remote control may be unavailable when a robot needs attention. Relay Robotics and YardOS offer competing fleet-management products, giving warehouse operators alternatives as they evaluate remote fleet-management tools. The presence of Relay Robotics and YardOS means HelioDock’s new software will compete against products already aimed at managing robot fleets.

muse-spark-1.3: # Arcwell Launches Relay, an AI Assistant for Support Teams Support agents spend too much of their day hunting for information their companies already have. Arcwell thinks artificial intelligence can do the searching for them. The company has launched Relay, a platform that examines customer support conversations and suggests answers to agents. The tool is designed to help companies improve customer service by reducing the time agents spend searching for information. In addition to suggesting replies, Relay identifies customers who may cancel and creates summaries. The idea is to let agents focus on the conversation while the software handles the lookup work. CEO Priya Shah said, “Support agents spend too much time searching for information that their companies already have.” Arcwell tested Relay with 12 companies over six months. Agents using it closed tickets 14% faster, according to Arcwell. The company did not release customer-retention results, so it remains unclear whether flagging at-risk customers actually prevented cancellations. Relay costs $65 per agent each month, a per-seat price that puts pressure on Arcwell to show productivity gains that outweigh the subscription. Access is the other trade-off. Relay requires access to support transcripts and internal help documents to function. That depth of access is central to its usefulness — it is also what privacy and security teams will scrutinize. Arcwell says customer data is encrypted and is not used to train shared models, meaning information from one company does not improve the system for others. Companies can choose a 30-day or 90-day retention period, giving them some control over how long transcripts and related data are stored. For Arcwell, the launch is a test of whether practical, agent-facing AI can earn a permanent place in support operations. Faster ticket resolution is a promising start, but support leaders will be watching whether suggested answers stay accurate, whether churn warnings prove reliable, and whether the privacy safeguards hold up in procurement reviews.

Qwen: Qwen3.8 2.4T A95B: # Arcwell launches Relay to bring AI suggestions to support agents Arcwell has launched Relay, an artificial-intelligence platform that examines customer support conversations and suggests answers to agents, a product expansion for Arcwell and a new AI option for the broader customer service industry. Relay is designed to surface information companies already hold. The platform can recommend responses to agents, identify customers who may cancel and create summaries of support interactions. The answer-suggestion feature is intended to reduce the time agents spend searching for information, while the summaries can condense conversations for follow-up or review. The cancellation signal adds a predictive element, though Arcwell has not disclosed results tied directly to retention. Together, the features demonstrate how artificial intelligence continues to transform the customer service landscape, where response speed and case context are closely tied. Arcwell said 12 companies tested Relay over six months. Agents using the platform closed tickets 14% faster, the company said. Arcwell did not release customer-retention results, leaving open the question of how much the cancellation-warning feature affects churn. Relay costs $65 per agent each month, a pricing structure that ties the platform directly to the size of a support team. “Support agents spend too much time searching for information that their companies already have,” CEO Priya Shah said. The platform requires access to support transcripts and internal help documents, the material it needs to generate suggestions and summaries. That access may raise data-governance questions for buyers. Arcwell said customer data is encrypted and is not used to train shared models. Companies can choose either a 30-day or 90-day retention period. The launch extends Arcwell’s effort to help companies improve customer service using artificial intelligence. Relay’s mix of answer guidance, churn signals and automated summaries is aimed at making support work faster and more consistent. The next test will be whether the platform’s faster ticket closure can also produce clearer retention results.

muse-spark-1.3: 6

Qwen: Qwen3.8 2.4T A95B: 6

muse-spark-1.3: # Cloudnote Left Prompt Excerpts Exposed for Four Hours on Monday *The debugging page showed short snippets from 1,842 sessions; Cloudnote says it found no sign of wider access, but its investigation is still underway* Cloudnote left an internal debugging page that showed excerpts from customer prompts accessible on the open internet without authentication for four hours on Monday, the company said after being alerted by an outside researcher. Server logs reviewed by the company show the page displayed snippets from 1,842 user sessions during the exposure window, a small fraction of Cloudnote’s 3.2 million registered users. Each entry could include up to 200 characters from a prompt, but did not include full documents, passwords or payment information, which the company said are stored in separate systems. The exposure matters because prompts sent to productivity tools often contain unpublished work, personal notes and other sensitive material that users assume is private. Even a short excerpt can reveal the subject of a document or the nature of a question, making the details of who saw the page as important as what was on it. Cloudnote said it removed the page 19 minutes after the researcher’s notification and has so far found no evidence that anyone else accessed it. That assessment is preliminary; the company said its investigation is not complete, and it has not yet said whether it will notify affected users or how the page became public.

Qwen: Qwen3.8 2.4T A95B: # Cloudnote Debugging Page Exposed Customer Prompt Excerpts for Four Hours *The company says excerpts from 1,842 user sessions may have been visible before takedown, but it has not determined whether anyone besides a researcher accessed them.* Cloudnote, which says it has 3.2 million registered users, said Monday that a debugging page containing excerpts of customer prompts was accessible without authentication for four hours. The company did not say how the page became exposed or when it first detected the issue. It said the page was taken offline the same day. Logs reviewed by Cloudnote show the page contained excerpts from 1,842 user sessions. Each excerpt could include as many as 200 characters from a prompt. For a service built around processing user instructions, even short fragments could provide context about a person’s work or inquiries. Cloudnote said full documents, passwords and payment information were stored elsewhere and did not appear on the page. The company said it removed the page 19 minutes after a researcher reported it. Cloudnote has found no evidence that anyone besides the researcher accessed the material, but it cautioned that its investigation is not complete. That leaves open whether the excerpts were copied or viewed during the four-hour window, and what, if any, further notification to affected users may be required. The researcher’s identity and method of discovery were not publicly disclosed.

muse-spark-1.3: # Foundry Hall, Milwaukee Startup Home, Will Close Oct. 31 MILWAUKEE — After eight years as a fixture for early-stage companies on Milwaukee's near west side, Foundry Hall, the nonprofit startup accelerator, will close Oct. 31, ending an eight-year run that supported more than 200 young businesses. The organization opened in 2018 with a simple premise: give founders who lacked connections a place to work, meet investors, and learn how to grow. Since then, it says it has supported 212 startups that collectively employ 1,140 people. That history will end this fall, undone largely by the loss of a single funding source. Blank River Bank, its largest corporate sponsor, provided 38% of its annual budget and declined to renew its sponsorship. Executive director Camille Ross said staff spent months searching for a replacement funder to cover the gap. “We kept waiting for the replacement check that never came.” Without it, Ross said, Foundry Hall has enough money to stay open through October but not to run another full accelerator program. The board voted to wind down operations rather than limp forward. The closure will eliminate the jobs of six employees and displace 12 startups currently using its workspace for offices, meetings and workshops. For many alumni, the news landed less as a business decision than as the loss of a gathering place. Dev Malik, a founder who went through the program in its early years, recalled arriving with little more than an idea and pitching investors for the first time inside the building after months of working out of coffee shops. It is a sentiment echoed across the city's startup circles this week, where founders described late nights, shared advice and first hires made within Foundry Hall's walls. Blank River Bank said its decision was driven by a shift in its community investment priorities, not by concerns about the accelerator's work. City officials said they are trying to soften the landing for the companies that must leave. Deputy Mayor Susan Cole's office said it is talking with building owners and local universities about temporary space. Whether those conversations lead anywhere in time remains uncertain. The 12 current tenants have weeks to find new homes, and Milwaukee has few low-cost alternatives that combine office space with mentorship and investor access. Ross said staff are now focused on helping those companies and workers transition, forwarding resumes and making introductions. Foundry Hall was never the largest accelerator in the Midwest, but for eight years it offered proof that a modest building could change a business trajectory. After Oct. 31, that proof will have to live elsewhere.

Qwen: Qwen3.8 2.4T A95B: # The Last Program at Foundry Hall Foundry Hall will close on October 31, ending the nonprofit startup accelerator that opened in Milwaukee in 2018 with a simple promise: give early-stage companies room to grow, mentors to steady them, and a building that made their ambitions feel legitimate. Since then, it has supported 212 startups that together employ 1,140 people. Those numbers are the record, but the loss will be felt in smaller measurements—desk space emptied, routines interrupted, six employees facing the end of their jobs, and twelve startups using workspace that must leave. The closure follows the withdrawal of the hall’s largest corporate sponsor, Blank River Bank, which supplied 38% of its annual budget. The bank declined to renew its sponsorship, saying the decision reflected a shift in community investment priorities and was not tied to Foundry Hall’s performance. Executive director Camille Ross put it plainly: “We kept waiting for the replacement check that never came.” Foundry Hall has enough money to operate through October, but not enough to run another full program. That financial arithmetic arrived quietly. There was no single dramatic failure, only a gap that widened month by month while the staff kept preparing for the next cohort. The accelerator’s work had always depended on confidence: founders betting on ideas, mentors betting on founders, and sponsors betting that the city’s innovation economy would repay the attention. When the largest bet was pulled, the rest did not fall into place. For many who passed through, the building mattered because it offered a threshold between improvisation and belonging. Founder Dev Malik remembered his first investor meeting taking place inside Foundry Hall, after he had once pitched from the back table of a coffee shop. That is the kind of support that does not appear easily on a balance sheet. A hallway conversation can become a partnership. A borrowed conference room can make a young company sound ready. Now the city is trying to soften the landing. Deputy mayor Susan Cole said officials are speaking with building owners and local universities about space for the current companies. It is a practical response, and necessary, but it cannot preserve what Foundry Hall provided as a whole. A startup can survive relocation. A community is harder to rebuild. On its final days, the hall will likely remain what it has always been: practical, hopeful, and understated. There will be forwarding addresses, final events, equipment gathered, leases reviewed. Staff members will carry projects to completion while looking for what comes next. The companies inside will keep writing updates and trying to hit milestones set long before the closing was announced. The founders may scatter, but the record will remain. Foundry Hall is closing because the money ran out, but it did not fail in the work. Milwaukee will now have to decide how much of that work can continue elsewhere.

muse-spark-1.3: # The Tie That Matters: Top Support AI Costs Less Than Half **For customer-support teams, the most expensive model didn't win — it tied.** If you were buying customer-support AI on price tag alone, you would pick Model North. In a new benchmark built on 200 real, anonymized tickets from a software company, North earned the highest average quality score. The catch is what finished a whisker behind it. Model Vale scored within half a point of North, a difference the researchers say was not statistically significant, yet Vale cost less than 40% as much to run and answered 18% faster. For support leaders balancing quality, latency, and budget at scale, that effective tie is the story. It reframes the question from which model is smartest in the abstract to which model is good enough to deploy without paying a premium for no meaningful gain. ## A practical test, judged by humans The test itself was deliberately practical. Researchers gave each of five models the same 200 English-language tickets and asked for one proposed answer per ticket, mimicking a first-draft assistant for human agents rather than a fully autonomous agent. Three experienced support managers then graded every answer blindly, without knowing which model wrote what. Their scores combined three things customers actually care about: factual correctness, usefulness for resolving the issue, and tone. That human, blind grading matters because automated metrics often miss the difference between a technically correct answer and one an agent could actually send. Averaging across three judges also smooths out individual strictness, though with only 200 tickets it still leaves room for noise — which is why the statistical tie at the top is important. ## What the tie really means North averaged 84.2 out of 100, Vale 83.8. Treat that 0.4-point gap as what it is: undetectable to customers and managers. What is detectable is cost and speed. At an estimated $1.90 per 1,000 tickets for North versus $0.74 for Vale, a team handling a million tickets a year would spend roughly $1,900 versus $740 for draft generation alone. An 18% speed advantage for Vale may sound modest, but in a live assist workflow where agents wait seconds for a suggestion, it compounds across queues and shifts. The benchmark also hinted at different strengths beneath that tie. North handled billing disputes more cleanly, where policy precision and careful wording matter. Vale was stronger on technical troubleshooting, where step-by-step logic and completeness count. Neither edge was large enough to overturn the overall tie, but for a company drowning in one type of ticket, it could guide a pilot. Below that top pair, the trade-offs got starker. Two mid-priced models, Ember and Kite, scored in the high 70s while costing well under a third of Vale's price, and the cheapest model, Harbor, fell to 71.0 at just $0.08 per 1,000 tickets. Those gaps are large enough to notice in practice: more missing facts, less actionable steps, more editing before an answer could go out. They might still make sense for low-risk triage or internal suggestions, but not as drop-in drafters where North and Vale operated. ## Where everyone stumbled Perhaps most telling was where every model struggled. When a ticket depended on an image attachment — a screenshot of an error, a photo of a broken setup — performance dropped across the board. That is a critical caveat for real support desks, where “see attached” is common and context lives outside the text thread. A text-only draft assistant, however fluent, will miss what the customer already showed you. Teams will need to test multimodal handling separately or keep those tickets on a human-first path. The study has other limits worth stating plainly. All tickets came in English from a single software company, so results may not carry over to other industries, jargon-heavy products, or multilingual queues. Researchers also did not test agentic actions, tool use, or follow-up dialogue — no refunds issued, passwords reset, or back-and-forth clarification. This was a test of one-shot answer quality, not end-to-end resolution. Even so, the takeaway for buyers is practical. Start with blind evaluation on your own tickets, weigh cost and latency alongside quality, and do not pay more for a leaderboard lead that disappears under statistical scrutiny. In this test, Vale matched the leader where it counted and won where it costs. Until someone proves a meaningful quality jump on image-heavy, multilingual, or agentic work, good enough at less than half the price looks like the smart default.

Qwen: Qwen3.8 2.4T A95B: # For Support Tickets, Value Is the Clearest Winner When five language models answered 200 anonymized customer-support tickets, the top two were separated by less than half a point. Model North averaged 84.2, while Model Vale averaged 83.8, and researchers found that difference was not statistically significant. The more meaningful finding is that Vale delivered essentially the same quality as North at a much lower estimated cost and with faster response times. This makes the benchmark’s clearest story a practical one: for this support workload, the strongest case is not for the highest-scoring model, but for the model that combines near-identical quality with better economics. If a support organization cares about cost per ticket, latency, and scalability, Vale looks more compelling. North remains strong, especially on billing disputes, but its slight scoring edge should not be treated as decisive. ## How the benchmark worked The test used 200 anonymized English-language tickets from one software company. Each model produced one proposed answer per ticket. Three experienced support managers graded the answers blindly, using a combined score for factual correctness, usefulness, and tone. That rubric matters because support quality is not only about accuracy: a reply can be true but too terse, unhelpful, or poorly worded for a frustrated customer. The researchers also estimated cost per 1,000 tickets and measured speed. This helps readers judge production fit. In a real support queue, a model that is slightly better but much slower or more expensive may be less useful than one that is good enough, affordable, and fast. ## Why Vale stands out Vale’s score was close enough to North’s that the difference should not be read as a real quality gap. But Vale’s estimated cost was $0.74 per 1,000 tickets, compared with $1.90 for North, and Vale answered 18% faster. That changes the practical calculation. The question is not which model scored marginally higher, but which model can provide dependable draft answers at scale. Vale’s combination of quality, price, and speed makes it the clearest value choice. North has a notable strength: it performed best on billing disputes. Vale, meanwhile, performed best on technical troubleshooting. Those differences suggest that the models may fit different queues better, but they do not overturn the main result. Teams with a high volume of sensitive billing issues may examine North more closely, while Vale’s overall value remains strongest. The other models fell behind the leaders on average quality, and their lower prices do not appear to compensate for that gap in this test. Harbor’s $0.08 per 1,000 tickets is strikingly inexpensive, but its 71.0 average score suggests that the cheapest option may not be suitable where quality matters most. Ember and Kite occupied the middle of the field, but the benchmark’s main decision point remains between North and Vale. ## Shared weakness and limitations All five models struggled when a ticket depended on an image attachment. This is a major caveat for modern support, where customers often send screenshots, error messages, photos, or annotated interface images. The benchmark does not show that any of the models can reliably handle those multimodal cases. Readers should also keep the test’s boundaries in mind. The tickets came from one software company and were only in English, so results may not transfer to other industries, customer voices, or support policies. The benchmark also did not test agentic actions, tool use, or multilingual support. That means it evaluates proposed answers, not complete autonomous workflows. A production system might need to check account data, consult a knowledge base, update tickets, or follow company-specific rules. The lower-scoring models were not close enough to the leaders to challenge this conclusion, and the gap between top and bottom shows model choice still matters. But the clearest result is not a narrow victory for North. It is that Vale matches North statistically while offering lower cost and faster answers. For customer-support teams judging these models under this benchmark, Vale is the standout value option.

muse-spark-1.3: # Raincheck raises $8 million for flood-risk software *The Austin startup helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

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

muse-spark-1.3: # KiteBridge Software Error Sent $2.8 Million in Duplicate Payments to Workers **91 employers funded erroneous reimbursements; company says most funds recovered** Ninety-one companies were left scrambling to claw back millions of dollars this week after a software update from expense-management provider KiteBridge generated 13,400 duplicate employee reimbursements totaling $2.8 million. The erroneous payouts went to employees, but the cost fell on their employers, who fund reimbursements through the platform. KiteBridge said no employees had money withdrawn from their personal accounts as part of the cleanup, a distinction that has done little to ease frustration among finance teams now reconciling unexpected outflows. The failure began Tuesday morning with a routine deployment. At 8:45 a.m., KiteBridge pushed out version 4.2 of its platform. Within half an hour, the first sign of trouble appeared when a customer reported a problem at 9:12 a.m. Behind the scenes, the company later explained, its system had run into a timeout from its external payment processor — essentially, the outside service that moves money failed to confirm back in time that payments had gone through. Designed to avoid stalled payments, KiteBridge's fallback system automatically retried the transactions. Instead of recognizing those retries as copies of payments already in progress, the fallback treated each retry as a brand-new reimbursement request. For a general reader, think of it like pressing “send” again when an email appears stuck, only to have the recipient receive two, three, or more identical messages — except here each copy moved real money. That flaw runs counter to a safeguard KiteBridge had previously advertised. An April changelog for version 4.0 said the company had introduced “exactly-once payment protection,” industry shorthand for a mechanism meant to guarantee that even if a system retries, a payment is executed only once. KiteBridge suspended all automated payments at 10:03 a.m. Tuesday, a little more than an hour after deployment, halting further duplicates. Its public communication lagged behind its technical response. The company's public incident page initially described the problem only as delayed reimbursements, with no mention of extra money having been sent. It was not until six hours after automation was suspended that the page was updated to disclose that duplicate payments had been issued. By Wednesday evening, the company said $2.3 million of the $2.8 million had been recovered, with the remainder still pending return from receiving banks and employers. It did not detail how the reversals were being handled or how long full recovery would take. Chief Executive Lena Ortiz apologized for the incident and said the company would publish a full technical report explaining how the fallback bypassed its payment protections and what changes would prevent a recurrence. The incident underscores how fragile automated money movement can be, even with protections in place. For corporate customers, the immediate damage was not lost funds so much as disrupted cash flow, added reconciliation work, and uncomfortable conversations with employees who woke up to surprise deposits they did not request. Whether KiteBridge’s forthcoming report restores confidence may depend on how clearly it answers a central question: why a system explicitly built for exactly-once payments behaved as if that guardrail did not exist.

Qwen: Qwen3.8 2.4T A95B: # KiteBridge Update Created $2.8 Million in Duplicate Reimbursements A software deployment at expense-management company KiteBridge turned into a costly processing error this week, causing the system to issue 13,400 duplicate reimbursements worth $2.8 million across 91 corporate customers. The failure did not take money out of employees’ bank accounts, KiteBridge said, but it did force employers to fund the extra payments while the company raced to recover them. The trouble began Tuesday morning, when KiteBridge deployed version 4.2 of its software at 8:45 a.m. The first customer complaint arrived at 9:12 a.m., an early sign that reimbursement requests were being mishandled. What followed was a cascade created by a seemingly narrow technical problem: a timeout from an outside payment processor. In payment systems, a timeout can leave a transaction in limbo, so software often retries the request. KiteBridge’s fallback system, however, interpreted those retries as brand-new reimbursement requests rather than repeated attempts to complete the same payment. In practical terms, the software lost track of which payment had already been initiated and approved additional transfers for the same expense. The result was rapid and expensive. Before KiteBridge suspended automated payments at 10:03 a.m., the system had produced $2.8 million in duplicated reimbursements. Because the payments were made through corporate expense programs, the duplicate funds came from employers, not from individual employees. KiteBridge said no employees had money withdrawn from their accounts. The company’s public explanation also drew criticism. Its incident page initially described the issue as delayed reimbursements. It was not until six hours after automated payments had been suspended that the page disclosed the real problem: duplicate payments. That delay left some customers without a clear understanding of why their reimbursement flows had been halted or why extra transactions were appearing. By Wednesday evening, KiteBridge had recovered $2.3 million of the duplicated funds, leaving the remainder pending. The company did not say how long it expected the recovery process to take or whether affected employers would be made whole if some payments could not be returned quickly. The failure also raised questions about prior claims in KiteBridge’s product documentation. In April, the company’s changelog for version 4.0 said the release had introduced “exactly-once payment protection,” a safeguard meant to prevent the same transaction from being processed more than once. Such safeguards are common in financial software. Version 4.2, however, failed to preserve that protection when confronted with a payment-processor timeout, exposing a gap between the feature’s intended behavior and how the system handled real-world failures. Chief Executive Lena Ortiz apologized for the incident and said KiteBridge would publish a technical report. For customers, the immediate concern is likely to be whether the company can restore confidence that its expense platform can safely handle payment errors without creating new ones.