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@SowingAlphaSeed 2h ago

Farmer on X

Bought some $ABVX calls instead.

@convequity 4h ago

Convequity on X

Our least-favoured bet on next-generation nuclear: light-water SMRs. Smaller does not automatically mean cheaper. A 50–300 MW small modular reactor produces far less power than a 1 GW plant, but containment, site infrastructure and other costs may not shrink proportionately. Factory production and simpler safety systems can help, but those savings must outweigh the loss of scale. The result could be a higher cost per installed kilowatt, not a lower one. Advanced alternatives offer higher temperatures and more industrial uses. But we think many face a bigger obstacle than licensing: securing enough fuel. To understand the investment trade-offs, separate two questions: what cools the reactor, and what fuels it? Light water → Proven technology, established fuel supply. Uses ordinary water and fuel typically enriched below 5% uranium-235. Runs at around 300°C under pressure, producing electricity and lower-temperature heat. Its advantage is maturity; its limitation is that it cannot directly supply the hottest industrial processes. Shrinking it does not necessarily improve its economics. Sodium → Higher temperatures without high pressure. Transfers heat efficiently and operates near atmospheric pressure, reducing the need for heavy pressure-containing equipment. The catch: sodium burns in air and reacts violently with water, requiring strict separation and additional safeguards. Helium → High-temperature heat without a chemically reactive coolant. Can reach roughly 750°C or more, supporting more efficient electricity generation and industrial heat. Helium does not burn or react chemically, but the system operates under pressure and must stay leak-tight. Leading designs also require specialised TRISO fuel. Fluoride salts → High-temperature heat at low pressure, without sodium’s fire risk. The challenges are corrosion, suitable materials and preventing the salt from freezing. Some designs, such as Kairos Power’s, use salt to cool solid TRISO fuel. Others dissolve the fuel in the salt itself. These are different designs with different fuel requirements. Then come two separate fuel bottlenecks: HALEU is about uranium enrichment. It contains more than 5% but less than 20% uranium-235. Many leading sodium-, helium- and fluoride-salt-cooled designs need it, although not every advanced reactor does. Western production remains limited. Centrus Energy ($LEU) has produced small quantities with demonstration equipment and targets new enrichment capacity in 2029. We do not expect sufficient supply for broad deployment before the late 2020s at the earliest. TRISO is about fuel manufacturing — not a type of coolant. Tiny uranium-bearing particles are wrapped in carbon and ceramic layers designed to retain radioactive fission products even at roughly 1,600°C. That is a major safety advantage, but manufacturing and quality control are demanding. US production already exists, but capacity needs to expand substantially. X-energy’s ( $XE) larger TX-1 facility is still under construction. Helium-cooled and some salt-cooled designs therefore face both bottlenecks: getting HALEU, then turning it into finished TRISO fuel. For investors, a reactor licence is not a fuel supply chain. And a smaller reactor is not automatically a better investment.

@StableBread 4h ago

Fajasy on X

Even if the AI model Nubeva Technologies $NBVAF (TSXV: $NBVA) is training to read malware binaries (compiled programs) works, six risks could keep it from paying off: → Labs closing the gap: OpenAI’s GPT-6 Astra nearly doubled GPT-5.6-sol’s score on SRE-Bench, a Columbia University-led test of AI models on reading binaries, within a month of SRE-Bench’s release, and Anthropic says its Claude Mythos Preview model can already reconstruct plausible source code from stripped binaries. The better the labs’ own models get at reading hard binaries, the less they need to pay for outside labels. → Big tech building in-house: Microsoft’s $MSFT Project Ire reverse engineers software files on its own to decide whether they’re malicious. Ire caught only about a quarter of the malware among nearly 4,000 hard-to-classify files in a test Microsoft described in August 2025. But in June 2026, Microsoft said Ire flagged a new malware sample that most major security products, including CrowdStrike’s $CRWD and SentinelOne’s $S, still missed. Google $GOOGL already owns VirusTotal and Mandiant, so the likelier customers are labs that don’t own a malware-scanning site or an incident-response firm already, like OpenAI and Anthropic. → Paid like a contractor: Data labeling companies (firms that turn experts’ knowledge into training data for AI labs) pay 60-70% of what they bill to their contractors, and Scale AI is already recruiting its own malware analysts. So a data labeling company could pay Nubeva by the hour or task, or hire analysts itself, instead of licensing the model or buying the company. → Selling labels teaches the buyer: Every binary Nubeva labels helps the buyer’s model learn the same skill, the reason CEO Randy Chou gave for not patenting how Nubeva reads binaries. So each sale could shrink the edge Nubeva is selling. → Cash and compute: Chou gave no cost for running “hundreds and thousands” of AI agents on a private model. For reference, Nubeva spent $714,772 on R&D in FY2026, equal to 28.7% of its $2.49M in cash as of April 30, 2026. → Missed targets: Nubeva’s AI decryptor builder missed four targets starting in August 2023, so “days and weeks” (Chou’s timeline for the model) is a target, not a result just yet. So even with a working model, Nubeva still has to (1) sign a contract before the labs can read the hardest binaries themselves, (2) get paid for its model and engineers instead of by the hour or task, and (3) fund the training without draining its cash.

@calvinblissett 5h ago 1 click

Calvin Blissett on X

I kind of love this, sets up the capital slosh effect that happened in 2000s with the great rotation from tech to value. I think something similar happens here eventually and offers uncorrelated returns which will be more valuable. I will be eating dirt on value until then, clipping my coupons and buybacks. $KSPI $BOC $SLM $HHS $ZIM $CCLD $WU $HY $AMR $CNR $METC $HCC $FISV $YB $BTU $CNRD

@KEDM_COM 7h ago

KEDM.com on X

TIM Brazil $TIMS3.BZ / $TIMB will be buying back ~55m shares over the next 18 months. Not a huge buyback (2.3% of s/o), but this one is notable because s/o have been roughly stable since, what, 2014? Also, the stock is down 40% from ATH over the past few months while TIM keeps growing. We saw something very similar in 2024 (the price action, not the buyback), with the shares moving +100% the following year.

@calvinblissett 8h ago

Calvin Blissett on X

Sallie Mae ($SLM) 5.8x OE, Uber Cannibal (50% bought back since 2021), Outsider CEO, double digit EPS growth for years, 30% ROE. TAM just exploded. Franchise business model as a bank. 2.4% dividend https://t.co/CUmwbqd0HU Free writeup so please share if you find valuable or useful so I can do more free. Next 2 negative EV companies for founding subs only.

@convequity 9h ago

Convequity on X

We think $MNDY needs to make its platform verifiable to lead work management. MNDY has not disclosed its AI ARR in dollars. Our recent SaaS Survival Scorecard report estimates it at about $20 to $30 million as of the end of Q2 2026. Leading AI-native startups in other fields are far larger. In coding, Cognition reported crossing $1 billion in annualized revenue run rate on 25 September 2026, company-wide across Devin and Windsurf. In legal work, Harvey reported on 1 October 2026 that it crossed $400 million in ARR during Q3 2026. We think MNDY has long been highly adaptive, with great leadership and a strong recent cadence of features and products. But revenue growth has slowed, and in our view it has gained little from AI so far, because work management is a hard area for AI. In our view, coding agents suddenly became highly effective around early 2026, and work management has not yet had its own coding moment. Verifiability is the key difference: code can be tested, while most work management outcomes are hard to check. Rather than wait for that moment, how can MNDY make work management more verifiable? It could track and measure the actions taken on its platform and their outcomes, so that everything that happens there is verifiable. MNDY can learn from Palantir. Palantir launched AIP in 2023, its AI platform built on its ontology, and we think that ontology helped Palantir succeed with models much weaker than today's. Could MNDY build a similar substrate or ontology for work management in order to make the work done on its platform more verifiable? We do not claim to have the answer. Our view is that to thrive, and not only survive, MNDY needs to lead work management by making its platform verifiable, however it chooses to do that.