flessio
Joined 3 months ago
Karma
8
Submissions
14
Member since
Jul 14
Recent submissions
Comment · 1 day ago
Comment · 1 day ago
It's not only fakes - there is always a very large gap between what is available and what is listed online in honest buildings. Humans not marking move-outs or lease signings in realtime always creates a gap as do move-in dates. Buildings tend to only list what's available until the end of the month not 60 or 90 days out when you may actually want to move.
The best solution is to contact the building but this is time intensive and you get a lot of spammy followup which is why we created https://fless.io to automate this process and help you find what is actually available and not just what zillow or the management company's website lists. It's still the best way to know what is truly available.
Comment · 3 days ago
+1 - we've been very happy on Paseo.
We like the web + mobile app orientation (available without an app store!) along with the high degree of control.
Anyone moved between Paseo and T3? Curious to hear what motivated you in either direction.
Comment · 3 days ago
DeepSeek V4.1 Flash 1017, for a set of document extraction tasks we use, is the first model to step change move the cost per task down.
Serving via Deepinfra Flex at $0.0784 / $0.2352 / $0.0024 per M/tok is the least expensive cloud model we have ever benched by a wide margin.
This price / performance is probably the reason for the spike.
Comment · 27 days ago
A third party in... Singapore? China?
This is dead on arrival just like their, very good, powerpoint building product.
Nobody in the western world wants their value added closed source services (submit your H2 strategy deck to their cloud? Your contracts? I think not) especially when they are priced increasingly close to frontier.
Z.ai doesn't fully appreciate the level of distrust by their clients from western markets for simply being Chinese origin.
They do have credibility but it comes from being both open and having extremely capable models.
Removing one of these pillars makes a marginal risk / reward tradeoff probably a nonstarter.
Maybe LATAM, BRICS, Middle East are more willing to accept a closed / relatively expensive value prop. But not US / EU.
Comment · 1 month ago
It can only be this. And it will fail the same reason Alexa failed.
Nobody will blindly trust Amazon to buy products when they know Amazon cannot and will not truly suggest the best product for the consumer.
Alexa was supposed to be the voice gateway to purchasing. But nobody trusts the result Alexa picks because it will not default to the best value to the user - only the best value to Amazon.
Comment · 1 month ago
This.
Recently it was estimated that Amazon makes $30-80 billion(!) per year off not showing you the economically best results by deliberately disallowing sorting on $ per unit [1] to force sellers into ads. Amongst other tactics (ex. filter by country of origin).
AI agents can sort on $ / unit to easily find you the best deal, rendering Amazon's various forms of paid placement irrelevant.
Extend this to filtering Chinese slop brands (also not able to be filtered) and the significant portion of revenue starts looking like knocking back margins to the 3-5% range. Basically an existential threat.
Amazon cannot allow AI agents and maintain current margins.
Comment · 1 month ago
From what we hear / see in the community this will likely mean banning all abliterated / derestricted / decensored models on huggingface. And probably sooner than later.
Nvidia can't / won't tolerate headlines like "The scale of the problem is massive: Hugging Face, which hosts open source AI models, currently lists over 6,000 abliterated models."
In addition to this article, TC published another [1] just two weeks ago that agitates the same agenda.
Modelscope.cn seems to be one alternative (fewer reg-walls) but any others that have good selection of abliterated models? Especially those tuned to cyber / re team applications.
[1] - https://techcrunch.com/2026/09/03/abliteration-ai-is-making-...
Comment · 1 month ago
Absolutely makes sense.
Alongside our MCP, skill, public API, .md files for all pages, llms.txt and https://fless.io/ai-agents info page we also wired up detailed analytics at every stage and for every request isolated by channel, both human and AI agent.
We track all agent calls at a pretty granular level to understand the agent funnel, by channel and provider (ex. Perplexity User, OpenAI User etc..).
We can't see what the agent did after calling us but we can see a funnel clearly which is useful to see where engagement and drop-off are.
Our first goal is for users to start a new apartment search - moving from 'landing page' - one of the surfaces mentioned above - to search created (ie. we're hunting for apartments on their behalf).
This has been helpful to understand agent behavior and where we need to improve skill / MCP etc..
Comment · 1 month ago
We're working on an apartment hunting agent called Fless (https://fless.io).
Tell us what apartment you want.
We filter, sort and reach out to confirm apartments actually have a unit that meets your criteria by the move-in date you're looking for.
If they do, we schedule a tour for you and manage your calendar to ensure all the tours you book are nicely coordinated (with Uber rides scheduled if you need!).
No more emails, texts or spreadsheets and we also protect your privacy from post tour spam.
Once you find the unit you want we help you review the lease agreement, know what discounts to ask for and assist with scheduling movers who won't rip you off.
We're curious about how many people are using agents like Perplexity, OpenClaw, Hermes, Claude or others that support open skills and MCP to find an apartment.
We recently launched skills and MCP integration for agents at https://fless.io/ai-agents or via github / PyPI and other channels.
Check them out and let us know what you think - we respond to DMs!
Comment · 1 month ago
We've found the same. Agents are so sophisticated now that we haven't really focused on human users for our MCP and skill. Instead we have an agent page (https://fless.io/ai-agents) explaining where to find our MCP, what it does (finds apartments) and how to use it.
Combined with github presence and some other places agents just seem to find and use whatever works for them.
This is all largely invisible to end users who just use us through their agents to find apartments.
The exception is OpenAI which as you found out doesn't allow MCP for chat users (does for codex) instead requiring an app-store type listing. Not so open and triggers an app store style discovery process (and presumably tax in the near future).
Comment · 3 months ago
https://fless.io - find an apartment with no emails, phone calls or scheduling
Tell us what you're looking for (ex. move-in date, bedrooms, location, rent etc..)
We reach out to buildings that may offer what you're looking for
You get a calendar of tours (in-person or virtual) when buildings confirm they have exactly what you want
We schedule efficiently so you can easily check out buildings and don't have to worry about logistics (ex. how close is building A to C if I walk?)
Currently available in the DMV area but more areas and services (ex. verified moving companies, utilities switch, move-in/out cleaning) coming soon!
And now they are using AI tools to handle the emails related to renting including price negotiation.
There is a non-trivial amount of data science work put into these tools with the explicit goal of keeping rents high and using all available data (ex. your credit score, history at other buildings they own, your payment history, local pricing landscape and tons more) to extract the maximum you're willing to pay.
Virtually all large landlords use them on just about every channel - email, website chat, SMS and even phone now.
This is why we created https://fless.io to level the playing field and bring some of the same tactics to the renter side in addition to saving the emailing / scheduling hassle.