u/SlightReflection4351

I mostly work with platform engineering and infrastructure security buyers at mid market. The pattern that keeps killing my deals is that by the time marketing surfaces an account as "in-market", at least one engineer at that company has already started a poc with a competitor on their offhours. were not losing on product, were losing on tempo.

Last quarter i had 4 deals where i was brought in after the technical evaluation had already started. lost 3 of them. when i went back through the data, those accounts had been showing public engineer signals community joins, repo activity, tickets in open source projects for weeks before they showed up as in-market on our 6sense dashboard. we were just looking at the wrong layer. What im trying to figure out is whether there are platforms that actually catch the in market engineer at the individual level not the company level. 6sense, demandbase, clearbit all give me a company score. by the time the score moves im already late.what im trying to figure out is whether there are platforms that actually catch the in market engineer at the individual level not the company level. 6sense, demandbase, clearbit all give me a company score. by the time the score moves im already late. 
does anyone have a tool or like a process that catches in market eng accounts at the engineer-level signal stage.

willing to add to our stack if it actually helps, our average deal is large enough to make almost any tool ROI positive

I'm hitting an absolute brick wall trying to outbound into engineering orgs. We sell a developer focused tool and standard B2B intent data (we use 6sense and ZoomInfo) is giving us absolutely nothing. it flags an enterprise account as "in-market," our SDRs reach out to the engineering managers or directors, and we just get ghosted. I’m convinced it's because the actual engineers the ones running the evaluations, starring the repos, and looking for solutions are completely invisible on standard marketing trackers. They aren’t downloading whitepapers or filling out forms. How are you guys actually uncovering early developer intent, is there a way to see who is actually playing around with relevant tech before a PM gets involved and stalls the deal? Down for any workflows or tooling recs because our current pipeline is brutal.

Been testing agentless scanning across our AWS environment. full inventory within 24h, no agents deployed, no performance impact on running workloads. Biggest find: hardcoded credentials in old AMI snapshots we hadn't touched in months. Never would have caught that with agent based scanning since nothing was deployed on those assets.

One thing to know going in: it's a snapshot, not a live feed. still need an eBPF sensor or something similar for runtime threat detection. Not a replacement, more of a complement. What are you using for runtime coverage in agentless setups?

On a slide, our IAM story looks fine: Okta for SSO and MFA, AD/Entra for the Windows estate, SailPoint for governance. On paper that’s a modern stack. In practice, we still have fragmentation and whole sections of the application estate that none of these platforms really see.

The pattern is probably familiar. Okta knows about the federated SaaS apps. AD or Entra handles Windows resources and a subset of on‑prem apps. SailPoint does access reviews and lifecycle for whatever has a stable connector and someone took the time to onboard properly. Then there’s the identity dark matter: in‑house tools, old line‑of‑business systems, vendor apps with only a local user table or aging LDAP, and anything nobody had time to integrate. Those systems don’t show up in governance, aren’t consistently tied to HR as the source of truth, and orphan accounts there tend to be discovered by accident during an audit or an incident, not by design.

Concrete example: during a recent access review we found a former contractor still active in a local user table on one of these “nobody had time to integrate it” apps. Exactly the kind of thing we were hoping the stack would prevent, but it never saw the system in the first place.

We’ve already done the “integrate the platforms with each other” work. That helps, but it doesn’t magically pull in the disconnected apps. The real work seems to be a detailed inventory of every app that authenticates outside the main IdP or IGA paths, followed by hard decisions about which ones can be pulled into proper workflows for provisioning, access change, and deprovisioning. After that you’re left with the set that will stay edge‑cases and need different controls and explicit documentation so they’re not invisible.

For those of you who feel you’ve actually reduced identity dark matter in your environment, what specific change — inventory approach, orchestration layer, or governance process — had the biggest impact on getting those disconnected apps into a predictable lifecycle?

not looking for a policy template or a framework document. genuinely asking what tooling people are actually running for AI governance right now and whether it's holding up in practice.

context for us: we're about 600 people, fully distributed across four countries, mix of managed corporate laptops and personal devices, significant contractor population. the dev team is heavy AI users  Copilot, Cursor, a few other IDE tools, plus all the usual web-based stuff. the rest of the org is using ChatGPT, Gemini, various AI writing and productivity tools, AI features inside SaaS platforms we've approved for other reasons.

we built out an AI governance framework about 18 months ago. use case definitions, data classification guidelines, sanctioned tools list, acceptable use policy, the whole thing. got sign off from legal, compliance, and the CISO. spent a lot of time on it. the problem is enforcement has always been the weak link. the policy exists but the tooling to back it up has been stitched together from things that were built for different problems and it shows.

our CASB enforces some things on sanctioned tools but has no visibility into the browser extension ecosystem or IDE usage. our DLP catches some data movement but doesn't understand AI context  it can't tell the difference between someone uploading a file to a storage tool and someone pasting the same data into an AI prompt. our endpoint monitoring covers managed devices reasonably well but falls apart on BYOD and contractor machines.

the result is an AI governance program that looks complete on paper and has real gaps in practice. we know the gaps exist, we've documented them, we've been trying to close them for a while. just want to understand what other orgs at similar scale are actually running and whether anyone has found something that genuinely works across this kind of environment.

SASE and CNAPP are complementary layers that most orgs run completely disconnected. The real untapped benefit is dynamic policy enforcement: if your posture layer detects an attack path forming, your access layer should respond in real time.

Has anyone actually integrated these two operationally? What did the architecture look like

Hey everyone, I run a boutique ceramics brand and our shipping damage rate this month skyrocketed to 8%. We pack things like they’re going to war, but the traditional carriers just throw them around. It’s killing our margins and ruining the customer experience. What DTC shipping solutions are you guys using that actually treat packages with respect?

I’m a founder currently running early stage infra sales entirely by myself with no SDR team and no dedicated data ops so my time is incredibly limited and every single hour i spend pitching needs to count. I’ve been looking for sales intelligence tools that actually deliver for a bootstrapped startup because i need to avoid over buying massive enterprise features i will literally never touch. I would love to hear what your simplest stacks look like because right now outbound is completely founder led and i handle all the messaging myself through a basic cadence tool. Since we sell infrastructure i need accurate titles for technical buyers and real intent signals that are deeper than just an IP address visiting our site, plus the integrations have to be completely plug and play. I looked at the apollo route for volume and zoominfo seems powerful but the credit heavy pricing model stresses me out, while sales navigator requires way too much manual enrichment. It is hard to tell which of these new AI startup stacks are actually worth the monthly subscription when you just want something scrappy that won't eat your entire GTM budget and plugs into google sheets.

I'm launching a monthly curated food/drink subscription box targeting major cities. Since it's perishable items, standard ground shipping won't cut it. I need a robust last mile logistics platform that can handle scheduled, predictable deliveries. I don't just need basic ecommerce shipping software; I need an actual delivery partner with real-time tracking. What's the modern stack for this?

I’ve been experimenting with a bunch of ai prospecting tools to support our small sales team for the last six months, and i keep hitting the exact same problems over and over. Most of these tools just dump massive list of "interesting" companies or roles in your lap but half of them are totally irrelevant with the wrong tech stack or titles that are completely wrong. For our infrastructure product it is not enough to know they use AWS because i need deep technical context like their kubernetes distro or observability stack and which engineering teams actually own them. The automated outreach templates these tools generate feel so generic and fake since they dont reference concrete triggers that make a technical buyer actually want to respond. We waste so much time chasing noisy low quality accounts while missing high fit buyers who have urgent needs, and our LLM messaging prompts fall totally flat because the tool data lacks accurate up to date context about the accounts infrastructure signals.

Deal changes happen ALL the time in our sales process and  its becoming a struggle to keep track of everything. One minute the terms are set, the next its a whole new conversation with different details. 😫

Its really overwhelming trying to stay on top of all the moving parts. Im always jumping between different deals, notes, and updates, and it feels like im losing track of key changes that could affect closing the deal.

Im looking for any tips, tools, or systems to stay organized and avoid missing important updates. Would really appreciate any advice!!!

Hey everyone,
I have been trying to grow my email list by using web behavior data, but I am kinda lost. Can someone tell me what's the best tool or strategy you've used or know to capture more engaged visitors and turn them into subscribers?